Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"9109",leadTitle:null,fullTitle:"Engineered Nanomaterials - Health and Safety",title:"Engineered Nanomaterials",subtitle:"Health and Safety",reviewType:"peer-reviewed",abstract:"Nanotechnologies are extremely diverse, bringing about new opportunities in human lives through countless applications. 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1. Introduction
Stress has a strictly defined physical science definition describing the force per unit area acting upon a material, inducing strain and leading to dimensional change. Biologically, stress has also been defined as the overpowering pressure that affects the normal functions of individual life or the conditions in which plants are prevented from fully expressing their genetic potential for growth, development and reproduction. In the agricultural regard, stress has been described as a phenomenon that limits crop productivity or destroys biomass. It has become traditional to divide stresses experienced by plants into two major categories: biotic and abiotic stresses. Biotic stresses originate through interactions between organisms, while abiotic stresses are those that depend on the interaction between organisms and the physical environment. Abiotic stresses include potentially adverse effects of salinity, drought, flooding, chilling, metal toxicity, nutrient deficiency, UV exposure, air pollution, etc. [1]. The abiotic stresses represent the factors that most limit the agricultural productivity worldwide. These stresses not only have an impact on current crop species, but they are also significant barriers to the introduction of crop plants into areas that are not currently being used for agriculture [2].
When plants are subjected to environmental stresses such as salinity, drought, temperature extremes, herbicide treatment and mineral deficiency, the balance between the production of reactive oxygen species (ROS) and the quenching activity of antioxidants is upset, often resulting in oxidative damage.
In plants, there are a number of possible ROS sources. These include reactions such as photosynthesis and respiration found in the normal metabolism of plants. This is parallel with the well-known idea that ROS are certain to be one of the products output by aerobic respiration. Pathways that are embellished during abiotic stress also result in ROS production, such as during the photorespiration reaction, where glycolate oxidases in peroxisomes result in superoxidase production. Nonetheless, new sources of ROS have been found recently in plants, such as NADPH oxidases, cell wall-bound peroxidases and amine oxidases. They are involved in ROS production in such processes as cell death and are highly regulated [3]. The ROS are associated with several forms of cellular damage. Since activated oxygen species such as superoxide (O2⋅−), hydrogen peroxide (H2O2) and the hydroxyl radical (⋅OH) can seriously disrupt normal metabolism through oxidative damage to lipids, protein and nucleic acids, plants possess a number of antioxidant enzymes that protect them from these potential cytotoxic effects [4–7].
Plant species and cultivars within a crop species differ greatly in their response to environmental stress. Plants with high levels of antioxidants, either constitutive or induced, have been reported to have greater resistance to oxidative damage [6–9]. Reports suggest that the extent of oxidative cellular damage in plants exposed to abiotic stress is controlled by the capacity of their antioxidant systems [10].
In general, two classes of nonenzymatic antioxidants are found. They are lipid-soluble membrane-associated antioxidants (e.g. α-tocopherol and β-carotene) and water-soluble reductants (e.g. glutathione, phenolics and ascorbate). Ascorbate peroxidase (APX), superoxide dismutase (SOD) and glutathione reductase (GR) compose enzymatic antioxidants and they are thought to search for H2O2 in chloroplast and mitochondria. Catalase (CAT) and peroxidase (POD) are the other enzymatic antioxidants and are able to remove H2O2 and can neutralise or scavenge oxyintermediates and free radicals [11]. Key enzymes involved in the detoxification of ROS are, namely, SOD, CAT, POD, APX and other enzymes implicated in the Halliwell and Asada cycle (ascorbate–glutathione pathway). Under stress conditions, these antioxidants enhance the activity of almost all of these enzymes [11]. Superoxide radicals that emerge as a result of stress in the plant tissues are transformed into hydrogen peroxide (H2O2) by the SOD enzyme [12, 13]. The accumulation of H2O2, which results from the canalisation reaction of the SOD enzyme and is a powerful oxidant, is prevented by the ascorbate–glutathione cycle. The hydroxyl radical (OH), which is very reactive and the most toxic oxide, can react with all macromolecules without discrimination. SOD and CAT, by combining their actions, can prevent or decrease the formation of this oxide. Even though the particular scavengers are not fully known of the single radical oxygen or the hydroxyl radical, it is thought that SOD functions in removal via chemical reaction [11]. In the defence against intracellular antioxidants, SOD and GSH work together and SOD prevents the radical-mediated chain oxidation of GSH, thus helping GSH in its role as a free radical scavenger physiologically, without the accompaniment of oxidative stress [11, 14]. It was observed that with continued stress conditions, SOD enzyme activity, which acts by decreasing the oxidative oxygen species derived from stress, continued to increase. Even though the linearity of increased stress duration and the increase of SOD activity is concurrent, it was shown that genotypes with more tolerance are superior in this area. The CAT enzyme changes oxidative stress-induced reactive oxygen derivatives, like H2O2, into water and molecular oxygen [15]. CAT, found mostly in glyoxysomes of lipid-storing tissues in plants, contains a tetrameric haeme that catalyses the conversion of hydrogen peroxide, produced from the β-oxidation of fatty acids, into water and oxygen [11, 16]. The GR and APX enzymes, which are a part of the defence mechanism of tolerant genotypes against salt, drought and chilling stress, are generally effective in the reduction of hydrogen peroxide to water in chloroplasts and mitochondria, thereby detoxifying them [17, 18]. APX is one of the most important antioxidant enzymes of plants that detoxify H2O2 by using ascorbate for reduction. Different isoforms of APX are active in chloroplasts, cytosol and microsomes [11]. In the ascorbate–glutathione cycle, APX reduces H2O2 into water by oxidising ascorbate into monodehydroascorbate (MDHA), which is then converted into ascorbate via the MDHA reductase enzyme; thus, two MDHA molecules are changed into MDHA and dehydroascorbate (DHA) as a non-enzymatic side product in unequal amounts. Subsequently, the reduction of DHA occurs and ascorbate is produced by the action of dehydroascorbate reductase (DHAR) and GR. DHAR can then convert GSH into glutathione disulphide (GSSG) which then is reduced back into GSH by GR [18, 19]. Due to APX activity resulting in the need for regenerating ascorbic acid, it is thought that concurrently an increase in various other components of the antioxidative defence system is needed so that the protective mechanisms of plants can increase as necessary [11]. POD, CAT and APX appear to play an essential protective role in the scavenging process when coordinated with SOD activity. They scavenge H2O2 generated primarily through SOD action [11, 20].
The research indicates that APX, CAT, GR and SOD enzyme activities in large variation among cotton varieties [21], tomato [22–24] and melon genotypes [25] in their response to salinity have been observed.
The tolerance of plants to stress has been widely shown to vary with physiological growth stage, developmental phase and size of plants. There is also growing evidence of multiple tolerances to stress in plants with plants showing tolerance to more than one stress. Genetic variability within a species is a valuable tool for screening and breeding for higher stress tolerance.
2. Salinity stress
Salinity is one of the most important abiotic stresses that cause reduction in plant growth, development and productivity worldwide in arid and semi-arid regions, where soil salt content is naturally high and precipitation can be insufficient for leaching. The FAO estimates that 34 million hectares of irrigated land are salt-affected worldwide, and an additional 60–80 million hectares are affected by waterlogging and related salinity [26].
Salt stress changes the morphological, physiological and biochemical responses of plants. There is evidence that high salt concentrations cause an imbalance in cellular ions, resulting in ion toxicity and osmotic stress, leading to the generation of ROS, which cause damage to DNA, lipids and proteins. At the same time, ROS cause chlorophyll degradation and membrane lipid peroxidation, decreasing the membrane fluidity and selectivity. To prevent the negative effects of ROS, plants have developed various antioxidant enzyme systems including non-enzymatic antioxidants (e.g. ascorbic acid, glutathione and carotenoids) and antioxidative enzymes (e.g. GR, SOD and APX). While CAT and peroxidases detoxify the toxic hydrogen peroxide, superoxide is broken down into water and oxygen by catalyses from the SOD enzyme. APX reduces H2O2 using ascorbate as an electron donor in the ascorbate–glutathione cycle. Oxidised ascorbate is then reduced by GSH generated from GSSG catalysed by GR at the expense of NADPH. Previous studies showed that the level of antioxidative enzymes increases when plants are exposed to oxidative stress including salinity [27–29].
Plants with high levels of antioxidants, either constitutive or induced, have been reported to have greater resistance to oxidative damage. Genetic variability within a species is a valuable tool for screening and breeding for higher salt tolerance. Some authors have reported large variation among cotton varieties and tomato genotypes in their response to salinity. The response of plants to salt stress is variable and dependent on various factors, particularly plant genotype, extent of stress, age of plant and stage of plant growth when stress is experienced. Plants manifest themselves with various adaptive mechanisms (morphological, biochemical, enzymatic and physiological) to survive under stressed conditions. The extent of these mechanisms’ adaptability is unique with every plant genotype [30].
In one recent study, the relationship between antioxidant enzymes and salt tolerance in the leaves of eggplant seedlings of two salt-tolerant varieties (Burdur Bucak and Mardin Kızıltepe) and two salt-sensitive genotypes (Giresun and Artvin Hopa) was examined. In salt-tolerant eggplants, APX, CAT, GR and SOD activities increased significantly when the seedlings were grown in the hydroponic system containing 150 mM NaCl [31]. It was observed, in the results of pumpkin salt tolerance studies, that in the salt-sensitive genotypes (CU-7 and A-24) less of the aforementioned enzymes showed activity as opposed to the increased activity seen in higher salinity levels and in the salt-tolerant genotypes (Iskenderun-4 and AB-44). It was also shown that these enzymes played a role in salinity tolerance of melons [32, 33], green bean [6] and soybean [8].
The salinity experiment in okra genotypes shows that NaCl-induced stress caused decreases in plant biomass, green pigments, photosynthetic activity, stomatal conduction, transpiration rate, number of stomata and stomatal size and resulted in alterations in enzymatic activities (SOD, POD and CAT) and osmolyte accumulation (proline, glycine betaine, total free amino acids and total soluble sugars). The increase in Na and Cl and lipid peroxidation under saline conditions is the indication of ion toxicity and oxidative damage. However, the oxidative damage is controlled by a defensive system comprising various antioxidants, such as SOD, POD and CAT. The results depicted that salt-tolerant and salt-sensitive genotypes exposed to NaCl stress showed the highest activities of SOD, POD and CAT, both in root and leaf tissues of okra genotypes [30].
According to Yadav et al. [34], plants with high levels of antioxidants have been reported to have greater resistance to this oxidative damage and an increase in the activity of antioxidative enzymes in plants under salt stress. It was observed that increased levels of antioxidants in plants resulted in them having a stronger resistance against the oxidative damage, while plants under salt stress were shown to have the activity of antioxidative enzymes to become greater and that a correlation was seen between tolerance against salt levels and the amount of enzymes. It was also observed that in pea cultures, antioxidant enzyme activity increased when in saline environments, but SOD was unaffected in cucumber. The variations in these observations maybe due to the fact that the effects of salinity depend on a number of factors, for example, salt type, their concentration, plant genotype, growth stage and/or environmental conditions. The mechanism by which salinity affects the antioxidant responses is not yet clear. However, proposed that it might be via the change in membrane integrity caused by high Na+ to Ca2+ ratio [34].
Salinity inhibition of plant growth is the result of low osmotic potential of soil (water stress), nutritional imbalance, specific ion effect (salt stress), or a combination of these factors. Grafted plants also exhibit phenotypic variations from scion and rootstock plants in terms of salinity tolerance, and grafting onto salt-tolerant rootstocks capable of inducing salt tolerance in the grafted shoots has been an effective method for improvement of salt tolerance in agricultural practices [35]. In parallel, Solanum lycopersicum L. (‘Elazığ’) grafted on Nicotiana tabacum L. (‘Samsun’) and Nicotiana rustica L. (‘Hasankeyf’), namely “Tomacco” plant (patent no. TR-2008-05391-B), to 10-d high NaCl irrigation. Physical development, chlorophylls a and b, total chlorophyll, total carotenoid and anthocyanin levels were evaluated [36]. During an increase in osmotic stress levels, plants utilise various antioxidant enzymes and increase their activity; thus these enzymes play vital roles in ROS removal. SOD, which is an important scavenger, is used for producing water and oxygen by catalysing O2⋅−; afterwards the ROS hydrogen peroxide is catalysed by POD and CAT to produce water and oxygen as well [37]. It was reported by Azevedo Neto et al. [38] that antioxidative enzymes (e.g. POD, SOD, GR, CAT and APX) in addition to low molecular mass antioxidants make up the complicated antioxidative defence system seen in plants. It was observed that grafted seedlings when under stress by over the normal amount of Ca(NO3)2, contents of hydrogen peroxide and malondialdehyde (MDA), had a much lower percentage of electrolyte leakage and O2 production rate when compared to those of non-grafted seedlings from which can be deduced that there was less damage to the membrane of grafted seedlings than that of non-grafted seedlings when under excess Ca(NO3)2 stress. It was also observed that in grafted seedlings, free radical scavenging systems had a significant job when battling salinity stress, thus resulting in salinity tolerance [38]. In parallel with these results, Wei et al. [39] determined that due to the efficient scavenging system of free radicals in addition to the mechanisms that utilise antioxidative enzymes and polyamines for protection, eggplant seedlings that were grafted had a greater resistance against stress of over the normal amount of Ca(NO3)2 than when compared to non-grafted eggplant seedlings. For rootstock, they used the eggplant cultivar, Swartz cv. Torvum Vigor, which were tolerant to salinity [39]. Plant tissue culturing has been important in many fields including both agriculturally and commercially and has a significant role in the production of ornamental plants. It has also been invariably useful in manipulating plants for enhanced agronomic performances. In vitro culturing of plant cells provides a means for conducting many studies and improvement in scientific research, including helping to study plant physiology and genomics and their processes and enhancing genetic variability by providing the possibility to help in the breeding of improved cultivars. Thus, it has attracted a large amount of interest in later years. Regenerated plants are expected to have the same genotype as the donor plant; however, in some cases, somaclonal variants have been found among regenerated plants [40]. According to Kusvuran et al. [18], the responses to salt and drought stress of four pumpkin varieties (A-24, CU-7, Iskenderun-4 and AB-44) were investigated under in vitro culture conditions. In this study, it was observed that the tolerant genotypes had less damage on their cell walls (by lipid peroxidation) than that of the sensitive genotypes. While, it was observed that salinity stress had greater effect on the pumpkin genotype than stress by drought. The results of this study suggest that antioxidative defence mechanisms were effective in the pumpkin callus tissues during salt and drought stress. Thus, the increased activity of antioxidative enzyme activity as well as the lower increasing MDA content in the salt- and drought-adapted cells compared with the unadapted cells may contribute to salt and drought tolerance. The results obtained with the callus tissues are in agreement with those observed in studies using seedlings. The results demonstrate the selection of tolerant genotypes for oxidative stress such as drought; salinity could be used for in vitro methods [18]. Similarly, other research on squash, eggplant and melon indicated that SOD, CAT and APX enzyme activities in salt-tolerant genotypes are higher compared to salt-susceptible genotypes in both seedling and callus tissues [41–43].
Antioxidant enzymes such as SOD, POX and CAT are known to substantially reduce the levels of superoxide and hydrogen peroxide in plants. It is one of the most important enzymes used against oxidative stress in the plant defence system, and it occurs ubiquitously in every cell of all types of plants. The most common isoforms of SOD known in the literature are copper–zinc containing SOD (Cu/Zn-SOD), manganese containing SOD (Mn-SOD), iron containing SOD (Fe-SOD) and nickel containing SOD (Ni-SOD) [44]. Moharramnejad and Valizadeh [45] observed the three isoforms for SOD and POX and one isoform for CAT. Authors showed that in salt stress, the main activities of SOD, POX and CAT isozymes are significantly higher than normal conditions in red bean (Phaseolus vulgaris L.).
3. Drought stress
Drought stress is one of the most serious abiotic stresses that cause a reduction in plant growth, development and yield in many parts of the world [46–48]. However, plants have developed different morphological, physiological and biochemical mechanisms to withstand drought stress [49–51]. It is of considerable value to understand the reactions and responses of plants in drought environments as it is also a major part in making crops more tolerant towards stress. It is important to note that responses of plants towards water stress are seen to be considerably different at organisational levels according to the intensity and time they spend under the stressful environment as well as the period of growth they are in and their species [52]. When water becomes limited, the plant generally experiences stomatal closure in an effort to prevent further water loss, limiting the carbon dioxide available for fixation by photosynthesis and reducing NADP+ regeneration by the Calvin cycle [53]. These converse conditions increase ROS, such as hydrogen peroxide, superoxide, singlet oxygen and hydroxyl radicals [6, 54]. These ROS attack lipids, proteins and nucleic acids, causing lipid peroxidation, protein denaturing and DNA mutation. Plants possess several antioxidant enzyme systems that protect their cells from the negative effects of ROS. The role of antioxidant enzymes (APX, CAT, SOD and GR) as the components of the main tolerance mechanism is developed in response to different stress conditions. Many reports suggest that the extent of oxidative cellular damage in plants exposed to abiotic stress is controlled by the capacity of their antioxidant systems and the relationship between enhanced or constitutive antioxidant enzyme activities and an increased resistance to drought stress [55–57].
In one study [58], the effects of drought stress on plant growth, relative water content (RWC), ion concentration and activities of the antioxidant enzymes, APX and GR, in eight okra genotypes were investigated. Drought-resistant genotypes exhibit a better protection mechanism against oxidative damage by maintaining a higher inherited and induced activity of antioxidant enzymes than the sensitive genotypes. Previously, 31 different melon genotypes grown under salt and drought conditions were classified according to some growth parameters (i.e. shoot length, plant leaf area, leaf number, fresh and dry weight, leaf water content, ion accumulation and membrane injury index), as well as some antioxidant enzymes activities in vivo. At the end of the study, two salt- and drought-tolerant genotypes and two sensitive genotypes were selected according to the growth parameters measured. The aim of this study was to determine the activities of antioxidative stress enzymes in some salt- and drought-sensitive or salt- and drought-tolerant melon varieties grown in in vitro culture under salt and drought stress conditions. Another aim of this study was to determine whether in vitro callus culture can be used as a screening method for salt and drought stresses in a melon screening study. In contrast with the control, in all the different 8-day salinity and drought stress, it was discovered that growth prevention occurred in callus tissues within media containing 100 mM NaCl or 15% (w/v) PEG-6000 (polyethylene glycol). It was observed in the study from the MDA measurements, from which the amount of lipid peroxidation of the cell wall can be deduced, that the amount of damage to the cell wall (by lipid peroxidation) was more in the sensitive genotypes than that of the tolerant genotypes. The antioxidant enzyme (APX, CAT, SOD and GR) activities were investigated in the callus tissues of four melon genotypes under salt and drought stress. This study suggested that antioxidative defence mechanisms were effective in the melon callus tissues during salt and drought stress. The results demonstrate the selection of tolerant genotypes for oxidative stress such as drought; salinity could be used for in vitro methods [59]. Drought tolerance of tomato genotypes [60] was investigated and found biochemical changes (drought stress index, MDA content and antioxidant enzyme activities) that occur as a result of stress in plants were investigated. In salt-tolerant varieties, T-1 and T-2, the decrease of ions occurred at lower levels under drought conditions. APX, CAT, SOD and GR enzyme activities have increased in drought stress conditions. The four varieties showed an increase in MDA content under drought conditions, especially in the sensitive genotypes. The results indicate that the tomato seedlings respond with enzymatic defence systems against drought-induced oxidative stress.
Water stress tolerance is seen in all plant species but its extent varies from species to species. Effects of different PEG concentrations with drought stress on the activity of antioxidant enzymes, CAT and APX, were investigated in two melon genotypes. Drought tolerant (CU-196) and drought sensitive (CU-3) were grown in hydroponic conditions. Recently, PEG has being used as osmotic pressure inducer in drought physiology studies. In the study, 15, 30 and 45 mM PEG-6000 doses (–0.15 MPa, –0.52 MPa and –1.50 MPa, respectively) were compared to CAT and APX antioxidant enzymes in tolerant and sensitive melon genotypes. At the end of the study, CAT and APX enzyme activities significantly increased in CU-196 than CU-3 [61]. In the other study was conducted for determination of tolerance levels to drought of melon genotypes (Midyat, Şemame, Yuva and Ananas) that have determined the levels of tolerance to salt stress. In this study, three different irrigation methods have been applied to plants. (S0: control-plant-available water, 40% is consumed for irrigation, S1: plant-available water, 90% is consumed for irrigation, S2: during the period of 3–4 leaves of plants completely cut off from the irrigation). Morphological and biochemical changes that occur as a result of stress in plants were investigated. Drought stress applied to the visual scale evaluation of melon genotypes in terms of Midyat and Şemame melons had values close to controls. However, Yuva and Ananas genotypes were found to be more pronounced losses caused by drought. Under drought stress, for Midyat and Şemame genotypes that are tolerant to salt stress, plant fresh and dry weight, the values shown in chlorophyll were closer to control values. At the same time, SOD, CAT, GR and APX enzyme activities have increased in drought stress conditions. However, the susceptible varieties (Yuva and Ananas) compared to the control plants in terms of the parameters studied enzyme activities decreased to varying degrees. In general, it also drought-tolerant melon genotypes found to be tolerant groups, respectively. In particular, these melon varieties have enhanced levels of antioxidant enzyme activities by activating the tolerant concluded [62]. Kiran et al. [60] investigated that determination of tolerance levels to drought of tomato genotypes (TR-68516, Rio Grande, TR-63233, TR-63233 and H-2274). Authors indicated that SOD, CAT, GR and APX enzyme activities have increased in drought stress conditions. On the other hand, in the susceptible varieties (TR-63233 and H-2274) compared to the control plants in terms of the parameters studied enzyme activities decreased to varying degrees [60].
Smirnoff [63] indicated that oxidative damage is also manifest in effects on to proteins and nucleic acids, although these are rarely measured and can be affected by other factors. Oxidation of amino acid residues can be followed by the loss of catalytic activity and denaturation. The damaged proteins may be more susceptible to proteolytic degradation. DNA repairing enzymes may also be induced as a result of oxidative damage to DNA. The various repair and protection systems found in plants decrease the amount of open oxidative damage and consist of two groups. One of the groups includes CAT, ascorbate, PODs and SOD where in these systems react with oxygen forms that are active and keep them at a minimum level. The second group consists of GR, mono and DHARs, glutathione (GSH) and ascorbate, which are involved in the regeneration of antioxidants that are oxidised. For the purpose of maintaining the superoxide concentration at a minimum, and thus decreasing the production of hydroxyl radicals by the Haber–Weiss reaction of which the catalysis are done by metals, superoxide is converted into hydrogen peroxide by the SOD enzyme catalysis from the first group. Three types of SOD occur in plants: Cu/Zn-SOD, Mn-SOD and Fe-SOD. The latter two have similar amino acid sequences. SOD isoforms occur in most of the subcellular compartments (hydrogen peroxide is broken down to water by CAT which is located in peroxisomes and glyoxysomes). In the chloroplast, this function is fulfilled by ascorbate that also has a cytosolic isoform. In plants, a large amount of the PODs can have major roles other than antioxidants, while ascorbate, with both superoxide and singlet oxygen, has the ability to react non-enzymatically too. In the second group, GSH and ascorbate are the key players in the reactions involved in antioxidants becoming regenerated. In the reaction involving APX, the MDHA radical is the major product and goes on to react with NAD(P)H-dependant monodehydroascorbate reductase (MDHAR) to get reduced to ascorbate. Or, ascorbate and dehydroascorbate (DHA) can be produced by the non-enzymatic reaction between two molecules of MDHA, after which ascorbate is produced by the reduction of DHA by GSH and where the enzyme DHAR catalyses the reaction, with the second product as oxidised GSH (GSSG). An NADP-dependant GR can then reduce the oxidised GSH into GSH. In the chloroplast, these reactions, sometimes known as the Halliwell–Asada cycle, result in the catalysis of the light-dependant reduction reaction from hydrogen peroxide into water by the action of the reductant (NADPH) that is produced by photosynthesis. The key players of this cycle ascorbate and GSH, along with their isoforms (GR, DHAR and MDHAR), are found in large amounts in the chloroplast in addition to other subcellular compartments. The oxidation of GSH pools, which are also essential in keeping sulphydryl groups of enzymes in reduced forms, could result in enzymes that rely upon these reduced SH groups being inactivated [63].
Yasar et al. [64] investigated 38 genotypes of different pumpkin species for the relationship between the drought tolerance capacity and antioxidant enzyme activity. As a result, it was observed that the enzyme activities are extremely vital in the drought tolerance of the pumpkin genotypes, such as under dry conditions, the drought-tolerant pumpkin genotypes use antioxidative enzymes more actively compared to the drought susceptible genotypes. The genotypes exposed to drought stress had relatively inferior SOD enzyme activity compared to their controls. However, the CAT enzyme activities of these genotypes were found to be increased. Alternatively, the opposite situation was also observed; if the CAT enzyme activities were decreased compared to the controls, the SOD enzyme activities were observed to be increased compared to the genotypes in control group. However, such a relationship was not established for the APX enzyme activities [64].
Drought tolerance in black pepper is attained through osmotic adjustment and better ROS scavenging machinery, functioning through different antioxidant enzymes. The activities of antioxidant enzymes such as SOD and POD become higher during stress in tolerant variety [65].
4. Chilling stress
Low-temperature, or chilling, stress (damage caused by low, but above-freezing temperatures) has been recognised as a unique environmental impact on crop plant physiology [66]. The damage resulting from the symptoms of the chilling stress includes a decrease in growth and yield of the plant. These symptoms consist of the prevention of metabolic processes, rise in the permeability and seepage through the cell membrane due to alterations in the order of the molecules or in the physical form, wilting and chlorosis [67]. Prasad et al. [68] has suggested that mitochondria are critica1 organelles in the metabolic production of energy in the cell. The competence and the stability of mitochondria are very important for the seedlings to survive low-temperature stress, especially during early seedling growth. Low temperature induces oxidative stress in the cell [69]. Under aerobic conditions, superoxide radicals and H2O2 are found to be normal metabolites of plant cells [70, 71] as well as animal cells [72, 73] and are kept at low, steady-state levels by the action of antioxidant enzymes such as SOD, CAT, GSH POD and APX located in the organelles and cytosol [74–76].
Active oxygen species (AOS) has been proposed to be responsible for cold-induced injury because they are produced at higher concentration during cold stress and may initiate degradative reactions, causing lipid peroxidation, membrane deterioration, protein degradation and chlorophyll quenching. An efficient antioxidant activity is essential in order to maintain the concentration of AOS at relatively low levels [67]. On the other hand, the damage that occurs during chilling stress accompanying illumination was thought to be mediated by an oxygen radical. The defence mechanisms of the cell against oxidative stress involve antioxidants that can be found in many plant organs in large amounts to perform vital biological functions. These include the enzyme systems CAT, SOD and numerous PODs, e.g. APX and guaiacol peroxidases (POX) [69]. During photosynthesis, superoxide and hydrogen peroxide are produced as side products and need to be removed. This is achieved by the SOD enzyme and the enzymes GSH and ascorbate from the ascorbate–glutathione cycle. It was shown by Aroca et al. [77] that due to ROS being produced under chilled environments during light-induced photo-oxidation, the major damage from chilling stress occurs during this time. The reason for ROS production under these circumstances is because of the slowing down of the enzymes involved in the Calvin–Benson cycle, thus resulting in the limitation of the NADP+ supplements receiving the electrons from the electron transport chain and inducing oxygen to absorb more energy than needed. To decrease photo-oxidation under chilling, there are three important mechanisms. The first one involves avoiding production of ROS by diminishing electron transport chain; the second one involves scattering surplus energy in the form of heat via violaxanthin de-epoxidation, and the third one involves scavenging ROS produced by antioxidant compounds and enzymes. Additionally, the water-water cycle in the chloroplast, where electrons flow in photosystem II from water to photosystem I to reduce oxygen without O2 levels having a net change, is said to be an active mechanism that can disperse energy from over excitation when there is an environmental stress. SOD, GR and APX are also some of the antioxidant enzymes involved in the function of this cycle [77]. The enzyme SOD is located in the cytoplasm, chloroplast, mitochondrion and peroxisome and acts as the first line of defence mechanism against ROS by dismutating O2 into H2O2 [78]. Furthermore, the dismutation of superoxide radicals into H2O2 and oxygen is an important step in protecting the cell, and in that conversion, SOD is considered a key enzyme [69]. CAT also played a significant role in chilling tolerance and is especially important for removal of H2O2 in C3 plants. Exposure to low temperature may increase the amount of AOS not only in cold-sensitive but also in cold-tolerant plants. There was a correlation between the reduction in CAT activity and H2O2 accumulation [79].
Oxidative free radicals can be highly reactive towards cell components, and therefore, the ability of the cell to remove these undesirable species might be viewed as an important feature in improved resistance to chilling stress. The increases in the activities of CAT3 provide evidence for the increased production of superoxide and H2O2 in mitochondria of maize seedlings. Increases in superoxide and H2O2 can be expected in cases in which there is either high O2 uptake or decreased ability of the electron transport pathway, which increases potential for higher electron leakage to O2 for subsequent production of superoxide and H2O2 [68].
One major antioxidant that plays a role in the detoxification of ROS and plant protection against oxidative damage is glutathione. There are two versions in which glutathione can exist in which are the oxidised disulphide version (GSSG) and the reduced version (GSH). The function of glutathione as an antioxidant is mainly assigned to its reduced (GSH) version as this form is oxidised to form the oxidised (GSSG) version during its function as an antioxidant. Therefore, keeping the concentration of reduced glutathione, from the ratio GSH/GSSG, high is important for plants. The production of GSH can occur both in cytosol and the chloroplast in the leaves of the plant. Furthermore, in the ascorbate–glutathione cycle, GR catalyses GSSG reduction into GSH via donation of electrons from NADPH molecules. ROS detoxification in the chloroplast is known to be mostly carried out by the ascorbate–glutathione cycle, which is thus accepted as the main pathway in this process. In this cycle, ascorbate is also considered to be a major antioxidant in addition to GSH [80]. According to Prasad [81], rapidity with which GR enzyme was induced during the early stages of acclimation and remained induced during chilling and recovery clearly suggests that acclimation uniquely induces the antioxidant defence mechanism that is necessary for protecting the seedling from oxidative stress injury.
Chilling-sensitive pepper cultivars were investigated for SOD, CAT and POD enzyme activity under chilling stress condition. The results showed that the activity of CAT decreased, and both SOD and POD activities raised in two cultivars, However, permeability of plasma membrane was positively related to MDA content, SOD and POD activity and also negatively related to CAT activity variation. As low temperature treating was extended, permeability of plasma membrane, MDA content and POD activity increased and SOD and CAT activity decreased in two cultivars: Xiza No. 7 (less chilling-sensitive cv.) could maintain a higher protective enzyme activity, and permeability of plasma membrane and MDA content were low; Hajiao No. 1 (chilling-sensitive cv.) was quite the contrary [82].
Anderson et al. [83] indicated that the response of ascorbate and glutathione in mesocotyls to acclimation and chilling was generally the same as that in the coleoptile + leaf, although the increase in the total glutathione pools in response to acclimation was not as extensive. Induction of other antioxidants in the mesocotyl may reduce the need for GSH synthesis. In the roots, there was no effect of acclimation or chilling on any of the antioxidants tested. However, the fact that H2O2 levels in the roots were not greatly increased by chilling suggests that a modification of antioxidants was unnecessary to prevent oxidative stress [83]. Likewise, Lee and Lee [84] established that APX is also an important antioxidant enzyme in scavenging or utilising H2O2. Total APX activity increased when chilling stress occurred in the leaves of the cucumbers and seemed to be because of favoured induction of the isozymes APX-5 and APX-4. However, 24 h after the stress, the increase seen in APX activity was because of the favoured expression of the isoform APX-3 [84]. Chilling stress causes many physiological and biochemical changes. Kang and Saltveit [85] investigated that chilling tolerance in cucumber seedling radicals. Chilling seedlings with radicles 20-mm long for 48 h at 2.5°C inhibited subsequent growth by 36%, while it reduced the growth of 70-mm-long radicles by 63%. APX activity was higher in 20-mm-long radicals before chilling than in 70-mm-long radicles. It appears that higher APX, CAT and DPPH (the stable free radical 1,1-diphenyl-2-picryl-hydraz)-radical scavenging activities, and sustained APX activity during chilled and during subsequent growth at 25°C following chilling in 20-mm-long radicals corresponds with higher chilling tolerance. The activities of APX, CAT and DPPH appear to be positively correlated with chilling tolerance [85].
A study on the effects of chilling stress on two salt- and drought-tolerant and two sensitive pumpkin genotypes in callus culture [18] found that the tolerant genotypes showed lower increase in lipid peroxidation and a greater increase in APX, CAT and GR than the tolerant genotypes under stress conditions. It was observed that even though the increase of glucose, proline and fructose concentration went up with prolonging of the chilling effect in all the pumpkin genotypes, a much more significant increase was observed in the tolerant genotype than that of the sensitive genotype. Thus it can be concluded from these results that in pumpkin genotypes, chilling stress results in an increase in the peroxidation of lipids and in oxidative stress, due to reactive oxygen radical production. Song et al. [86] in their study observed that chilling stress, SOD and CAT activities decreased in some extent in both cultivars, in comparison to control in tomato cultivars. Compared to control, chilling stress resulted in significantly higher POD activity in cv. Mawa on day 6, whereas no significant changes of POD activity caused by chilling stress were observed in cv. Moneymaker at all time points tested. On the other hands, APX activities were increased in the two cultivars under chilling stress. GR activities increased in cv. Mawa after chilling stress, but almost no change was observed in cv. Moneymaker [86].
5. Nutrient deficiency and toxicity of heavy metal
The micronutrients essential for the normal growth and development of plants, as it is known to be required in several metabolic processes [87]. Deficiency of nutrients such as Zn, Mn, Cu, Fe, Mg, B and K can modify the activities of several antioxidative enzymes [88]. Kosesakal and Unal [89] indicated that Zinc (Zn) is one of the essential micronutrients playing a significant role in many vital metabolic processes. Zinc deficiency is a major global problem hindering plant cultivation, and this problem is especially exacerbated in acidic calcareous soils, which is the most common soil type in arid and semi-arid regions of the world. It is known that magnesium deficiency results in the decrease of chlorophyll amounts in beans. It was observed by Welkie et al. [90] that in peppers, the amounts of chlorophyll and iron in leaves were directly proportional, while zinc was also shown to be beneficial in the synthesis of carotenoids and chlorophyll, thus being proved to be essential in the photosynthetic process in plants. Iron (Fe) is a cofactor of many antioxidant enzymes and could act as a pro-oxidant factor because free or loosely bound it catalyses free radical generation in the presence of reductants and peroxides through the Fenton reaction. The growth of sunflower plants under iron deficiency conditions affects POD isoforms differently, inducing a preferential reduction in activity of those isoforms involved in the detoxification processes [91].
Metal toxicities have received widespread attention as large amounts are released into the environment and affect living organisms. Heavy metal intoxication, especially by lead, cadmium, arsenic and mercury, constitutes serious threat to human health [92, 93]. Although information focussed on the relationship between heavy metals and oxidative stress in plants has been available in recent years, it is still difficult to draw a general conclusion about critical toxic metal concentrations in soils [94]. Heavy metals cause oxidative damage to plants, either directly or indirectly through AOS formations which are extremely toxic to living cells. Redox metals such as Cu or Fe appear to act directly on the production of AOS. Copper is among the major heavy metal contaminants in the environment with various anthropogenic and natural sources. Human health risk from heavy metal bioaccumulation in vegetables has been a subject of growing concern in recent years. Excess Cu inhibits plant growth and seed germination, induces chlorophyll degradation and interferes with photosystem activity. At the molecular level, Cu ions generate ROS. These reactive radicals cause oxidative damage of lipids, proteins and nucleic acids. Cu ions also are responsible for alterations of membrane integrity in plant cells. Cu-mediated membrane lipid peroxidation causes membrane damage, thus changing membrane permeability and leading to electrolyte leakage. Plants have evolved several antioxidant defence mechanisms to protect themselves from oxidative damage [95].
Zn is the second most abundant transition metal after iron (Fe) and is involved in various biological processes in organisms. Due to this, the results of the presence of Zinc were investigated, including zinc deficiency, hyperaccumulation and its protective role in plants. However, it is not clear what the implications of zinc stress are on antioxidant responses and the uptake of nutrition, though it is known that excess of zinc is not beneficial and can result in negative symptoms in plants. The symptoms that can be observed at the organism level include prevention of seed germination, of root development and of the growth of the plant, and chlorosis can be seen in the leaves. At the cellular level, excess Zn can significantly alter mitotic activity, affect membrane integrity and permeability and even kill cells. Investigates showed that Zn stress on the activity of many antioxidative enzymes (APX, SOD, POD and CAT) and antioxidant contents (ascorbate and GSH) in plants [96–105].
The availability of manganese (Mn) to plants is governed by redox processes, which depend on soil’s Mn reserve, pH and the availability of electrons. However, excess Mn disturbs the metabolism of plants and inhibits the plant growth. Mn causes deficiency of Fe, Mg and Ca and induces inhibition of chlorophyll biosynthesis and a decline in the photosynthetic rate. The toxic effects of heavy metals, both essential and nonessential elements, have been linked to the production of ROS. To quickly get rid of ROS, which result in the disruption of cellular metabolism due to damage by oxidative stress to important molecules, numerous enzymatic and non-enzymatic ways have evolved in living organisms. As Mn2+ plays a role in numerous processes, it is thought that an excess of it results in oxidative stress [106]. Cadmium (Cd) is a non-redox metal unable to participate in Fenton-type reactions. Naturally occurring amounts of Cd are normally low, however, the concentration can be significantly increased by anthropogenic activities. The impact of the uptake of Cd by living cells has been shown to be drastic, inducing oxidative stress and normally leading to cell death depending on the metal dose and time length of exposure [107]. In general, Cd in plants reduces growth, both in roots and stems, due to suppression of the elongation growth rate of the cells [94]. According to Dinakar et al. [93], cadmium is easily translocated from plant roots to above-ground tissues and potentially threatens human health. Cadmium in plants interferes with physiological processes, resulting in declined productivity. Cadmium can harness photosynthetic activity, chlorophyll content, plant growth and induce oxidative stress. ROS are efficiently eliminated by non-enzymatic (glutathione, ascorbate, a-tocopherol and carotenoids) and enzymatic defence systems such as SOD, APX, POD and GR, which protect plants against oxidative damage. The detoxification of O2 occurs due to the SOD enzyme, while H2O2 is detoxified by the enzymes CAT and PODs and thus OH radicals are not formed. In the detoxification of hydrogen peroxide from different compartments in the cell, glutathione reductase (GR) and APX are key players in the ascorbate–glutathione cycle. Glutathione is also the substrate for the biosynthesis. A constitutively high antioxidant capacity or increase in antioxidant level could prevent oxidative damage and improve tolerance to the oxidative stress established [108, 109]. Sandalio et al. [108] investigated effects of cadmium on antioxidative enzyme activity in pea. They said that the level of oxygen radicals in cells could be enhanced by a decrease of the enzymatic antioxidants involved in their detoxification, such as SOD.
It was recorded by Schützendübel and Polle [110] that antioxidative enzymes were prevented from functioning and that GSH was depleted for a short period by cadmium and other metals. It was also put forth that hydrogen peroxide accumulation resulted from the depletion in these antioxidants. These results were obtained by accessing models of antioxidative capacity. As more Cd tolerance was observed in plants when more GSH was synthesised, it can be deduced that the decrease in GSH levels is an important step for cadmium sensitivity [110]. Dong et al. [111] investigated that effect of Cd concentration in tomato seedling for antioxidative enzymes. From the results, it can be observed that POD and SOD activities significantly increased in plants that were given Cd with a concentration of 1–10 µM and that MDA levels also showed a significant increase, indicating that oxidative stress response was the result of Cd stress in tomato plants. Tanyolac et al. [112] reported that tolerance and protective mechanisms have evolved to scavenge free radicals such as superoxide, hydroxyl radicals and peroxides generated during various metabolic reactions. Antioxidative enzymes such as APX play a key role in controlling the cellular level of these radicals and peroxides. They found that APX activity was increased with Cu treatment [112]. Zhao et al. [113] investigated the different tolerance mechanisms to Cd stress between YSL189 and HZ903 at the seedling stage. When Cd concentration was >20 µM in the growing medium, the uptake rate of Cd was significantly higher in roots of YSL189 than in the roots of HZ903. When plants were supplied with 50- and 100-µM Cd in the growing medium, there were higher Cd concentration, higher biomass and plant height, shorter roots and higher expression levels of transporter genes natural resistance associated macrophage proteins (Nramp)2, Nramp3 and zinc and iron regulated transporter (ZIP) in roots of YSL 189 compared to HZ903. The high Cd accumulation in YSL189 was partly due to the higher Cd uptake rate and higher expression levels of Nramp2, Nramp3 and ZIP in its roots. At the same time, the degree of cell injury indicated by thiobarbituric acid reactive substance showed no significant differences in roots and stems between the two genotypes. The higher activities of SOD, POD and CAT in roots and stems of YSL189 were compared to HZ903 [113].
Xiong and Wang [95] indicated that Cu phytotoxicity in Brassica pekinensis. Cu treatments increased electrolyte leakage and POD activity, showing a significant correlation between Cu concentration in shoots with electrolyte leakage and POD activity. Oriental melon IVF09 was used as a scion, while the pumpkin Jinxinzhen No. 3 was used as stock to research the physiological characteristics of grafted melon (Cucumis melo) seedlings when copper stress was induced. It was observed from the results that copper stress resulted in the inhibition of the physical characteristics of the melon seeds. In the grafted seeds, as opposed to self-rooted seedlings, an increase was seen in the levels of glucose, photosynthetic pigments, fructose, the photosynthetic parameters, biomass, the phosphate and sucrose synthase activities, acid invertase and neutral invertase. When levels of Cu decreased and the levels of P, NA and K increased nutrients were taken up more easily. The concentration of CU in the leaves decreased by 31.3%, while a 15.2% decrease was seen in roots of the grafted seedlings when the levels of copper ion (Cu2+) stress became 800 µM and it was shown that grafting resulted in better endogenous hormone balance in the seedlings. When compared to the control, it was observed that grafted seedlings had a higher concentration of IAA and that POD activity was increased, while concentrations of ABA and maleic dialdehyde and the CAT and SOD activities became less. Thus, it could be deduced that grafting of melon seedlings was beneficial to them when under copper stress and relieved the resulting physiological characteristics from the stress, showing that the resistance of the grafted seedlings to copper stress increased due to grafting [114].
Shi and Zhu [106] indicated that the accumulation of ROS significantly increased in cucumber leaves exposed to excess Mn. It was observed that cucumber leaves in the presence of excess Mn resulted in higher activity of SOD, DHAR, POD and GR while adding SA (salicylic acid) resulted in the inhibition of the activities of APX and CAT, thus showing that different antioxidant enzymes had different changes. When the cucumber leaves were treated with SA, in the presence of excess Mn, the concentrations of the essential glutathione and ascorbate antioxidants increased [106]. Human health risk from heavy metal bioaccumulation in vegetables has been a subject of growing concern in recent years. It was observed by Kiran et al. that when under abiotic stress, mainly heavy metal applications, the Burdur Merkez and Burdur Bucak genotypes that were salt-tolerant had a higher resistance as opposed to the sensitive genotypes. The results also showed that drought, heavy metal and salinity stress resistance was observed to have evolved in similar ways in plants [115]. Another heavy metal lead (Pb) exerts adverse effects on morphology, growth and photosynthetic processes of plants; causes inhibition of enzyme activities, water imbalance and alterations in membrane permeability; and disturbs mineral nutrition [116]. Wastewater, which is used in agriculture in order to provide growing water demand, might be included heavy metal and trace elements. Lead is one of the most hazardous heavy metals, and it causes an extensive pollution in the environment, and also it has adverse effect on the growing of plants. In the other study was conducted to evaluate the effects of Pb stress in on lettuce (Lactuca sativa). It was found that SOD and GR were increased with oxidative stress [115].
6. Conclusions
In conclusion, both the callus tissue and whole plant studies show a positive correlation between increased antioxidant activity and different abiotic tolerance. Antioxidative enzyme activities play an important role against stress. The tolerance level against salt, drought and chilling stress in callus culture can be utilised as an effective criterion in the plants with other physiological criteria. Therefore, it can be said that antioxidative defence mechanisms and effective working systems in the aspect of tolerance against stress conditions in the plants. The literature suggests that tolerant and sensitive genotypes show different responses under abiotic stress conditions, that antioxidative enzyme activities play a protective role against abiotic stress and that antioxidative defence mechanisms are effective in providing resistance to stress in plants. The results of the studies showed that the young plants of the tolerant genotypes may have better protection against stress by increasing the activity of antioxidant enzymes under different abiotic stresses.
\n',keywords:"Drought, salinity, oxidative stress, ROS, chilling",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/49852.pdf",chapterXML:"https://mts.intechopen.com/source/xml/49852.xml",downloadPdfUrl:"/chapter/pdf-download/49852",previewPdfUrl:"/chapter/pdf-preview/49852",totalDownloads:2426,totalViews:935,totalCrossrefCites:8,totalDimensionsCites:29,hasAltmetrics:0,dateSubmitted:"March 23rd 2015",dateReviewed:"January 12th 2016",datePrePublished:null,datePublished:"February 17th 2016",dateFinished:null,readingETA:"0",abstract:"Climatic changes can cause serious reductions in yield and crop quality. Under the threat of climatic changes, one of the precautions to cope is selection and development of resistant vegetable genotypes to abiotic stresses. Several physiological and biochemical reactions and different tolerance levels can occur according to plant species. When plants are subjected to environmental stresses such as salinity, drought, temperature extremes, herbicide treatment and mineral deficiency, the balance between the production of reactive oxygen species (ROS) and the quenching activity of antioxidants is upset, often resulting in oxidative damage. Since activated oxygen species can disrupt normal metabolism through oxidative damage to lipids, protein and nucleic acids, plants possess a number of antioxidant enzymes that protect them from these cytotoxic effects. To control the level of ROS and to protect cells under stress conditions, plant tissues contain several enzymes for scavenging ROS. The high levels of antioxidative enzyme activities were determined in the tolerant genotypes of tomatoes, eggplant, peppers, cucumbers, melons, squash, beans, okra, etc. to several abiotic stress factors. Both the whole plant and in vitro callus culture experiments gave similar results. Antioxidant enzymes can be useful for screening to determine the tolerant and sensitive plant genotypes against abiotic stresses.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/49852",risUrl:"/chapter/ris/49852",book:{slug:"abiotic-and-biotic-stress-in-plants-recent-advances-and-future-perspectives"},signatures:"Sebnem Kusvuran, Sevinç Kiran and S. Sebnem Ellialtioglu",authors:[{id:"139032",title:"Associate Prof.",name:"Sebnem",middleName:null,surname:"Kusvuran",fullName:"Sebnem Kusvuran",slug:"sebnem-kusvuran",email:"skusvuran@gmail.com",position:null,institution:null},{id:"142251",title:"Prof.",name:"Sebnem",middleName:"Seküre",surname:"Ellialtioglu",fullName:"Sebnem Ellialtioglu",slug:"sebnem-ellialtioglu",email:"sebnemellialti@gmail.com",position:null,institution:null},{id:"176220",title:"Dr.",name:"Sevinc",middleName:null,surname:"Uslu Kıran",fullName:"Sevinc Uslu Kıran",slug:"sevinc-uslu-kiran",email:"sevinckiran@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Salinity stress",level:"1"},{id:"sec_3",title:"3. Drought stress",level:"1"},{id:"sec_4",title:"4. Chilling stress",level:"1"},{id:"sec_5",title:"5. Nutrient deficiency and toxicity of heavy metal",level:"1"},{id:"sec_6",title:"6. 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Plant Physiology and Biochemistry. 2011; 49(10): 1228–1237.'},{id:"B81",body:'Prasad TK. Mechanisms of chilling-induced oxidative stress injury and tolerance in developing maize seedlings: Changes in antioxidant system, oxidation of proteins and lipids, and protease activities. The Plant Journal. 1996; 10(6): 1017–1026.'},{id:"B82",body:'Zhirong Z, Guoyi L. The effect of chilling stress on membrane lipid peroxidation and protective enzyme in pepper seedlings [J]. Acta Agriculturae Boreali-Occidentalis Sinica. 1994; 1994-03.'},{id:"B83",body:'Anderson MD, Prasad TK, Stewart CR. Changes in isozyme profiles of catalase, peroxidase, and glutathione reductase during acclimation to chilling in mesocotyls of maize seedlings. Plant Physiology. 1995; 109(4): 1247–1257.'},{id:"B84",body:'Lee DH, Lee CB. Chilling stress-induced changes of antioxidant enzymes in the leaves of cucumber: in gel enzyme activity assays. Plant Science. 2000; 159(1): 75–85.'},{id:"B85",body:'Kang HM, Saltveit ME. Reduced chilling tolerance in elongating cucumber seedling radicles is related to their reduced antioxidant enzyme and DPPH-radical scavenging activity. Physiologia Plantarum. 2002; 115(2): 244–250.'},{id:"B86",body:'Song Y, Diao Q, Qi H. Putrescine enhances chilling tolerance of tomato (Lycopersicon esculentum Mill.) through modulating antioxidant systems. Acta Physiologiae Plantarum. 2014; 36(11), 3013–3027.'},{id:"B87",body:'Candan N, Tarhan L. Changes in chlorophyll-carotenoid contents, antioxidant enzyme activities and lipid peroxidation levels in Zn-Stressed Mentha pulegium. Turkish Journal of Chemistry. 2002; 27: 21–30.'},{id:"B88",body:'Yu Q, Osborne L, Rengel Z. Micronutrient deficiency changes activities of superoxide dismutase and ascorbate peroxidase in tobacco plants. Journal of Plant Nutrition. 1998; 21(7): 1427–1437.'},{id:"B89",body:'Kosesakal T, Unal M. Role of zinc deficiency in photosynthetic pigments and peroxidase activity of tomato seedlings. IUFS Journal of Biology. 2009; 68(2): 113–120.'},{id:"B90",body:'Welkie GW, Hekmat-Shoar H, Miller GW. Responses of pepper (Capsium annuum) plants to iron deficiency: Solution pH and riboflavin. Plant Nutrition—Physiology and Applications. 1990; 41: 207–211.'},{id:"B91",body:'Ranieri A, Castagna A, Baldan B, Soldatini F. Iron deficiency differently affects peroxidase isoforms in sunflower. Journal of Experimental Botany. 2001; 52(354): 25–35.'},{id:"B92",body:'Wennberg PO, Cohen RC, Stimpfle RM, Koplow JP, Anderson JG, Salawitch RJ, Wofsy SC. Removal of stratospheric O3 by radicals: In situ measurements of OH, HO2, NO, NO2, ClO, and BrO. Science. 1994; 266(5184): 398–404.'},{id:"B93",body:'Dinakar N, Nagajyothi PC, Suresh S, Udaykiran Y, Damodharam T. Phytotoxicity of cadmium on protein, proline and antioxidant enzyme activities in growing Arachis hypogaea L. Seedlings. Journal of Environmental Sciences. 2007; 20: 199–206.'},{id:"B94",body:'Gomes-Junior RA, Moldes CA, Delite FS, Pompeu GB, Grata PL, Mazzafera P, Lea PJ, Azevedo RA. Antioxidant metabolism of coffee cell suspension cultures in response to cadmium. Chemosphere. 2006; 65: 1330–1337.'},{id:"B95",body:'Xiong ZT, Wang H. Copper toxicity and bioaccumulation in chinese cabbage (Brassica pekinensis Rupr.). Environmental Toxicology. 2005; 20(2): 188–194.'},{id:"B96",body:'Mrozek JE, Funicelli NA. Effect of zinc and lead on germination of Spartina alterniflora Loisel seeds at various salinities. Environmental and Experimental Botany. 1982; 22: 23–32.'},{id:"B97",body:'Ebbs S, Uchil S. Cadmium and zinc induced chlorosis in Indian mustard [Brassica juncea (L.) Czern] involves preferential loss of chlorophyll b. Photosynthetica. 2008; 46: 49–55.'},{id:"B98",body:'Cakmak I. Possible roles of zinc in protecting plant cells from damage by reactive oxygen species. New Phytologist. 2000; 146 (2): 185–205.'},{id:"B99",body:'Auld DS. Zinc coordination sphere in biochemical zinc sites. Biometals. 2001; 14: 271–313.'},{id:"B100",body:'Stoyanova Z, Doncheva S. The effect of zinc supply and succinate treatment on plant growth and mineral uptake in pea plant. Brazilian Journal of Plant Physiology. 2002; 14: 111–116.'},{id:"B101",body:'Rout GR, Das P. Effect of metal toxicity on plant growth and metabolism: I. Zinc. Agronomie. 2003; 23: 3–11.'},{id:"B102",body:'Hacisalihoglu G, Kochian LV. How do some plants tolerate low levels of soil zinc? Mechanisms of zinc efficiency in crop plants. New Phytologist. 2003; 159: 341–350.'},{id:"B103",body:'Broadley MR, White PJ, Hammond JP, Zelko I, Lux A. Zinc in plants. New Phytologist. 2007; 173: 677–702.'},{id:"B104",body:'Lingua G, Franchin C, Todeschini V, Castiglione S, Biondi S, Burlando B, Parravicini V, Torrigiani P, Berta G. Arbuscular mycorrhizal fungi differentially affect the response to high zinc concentrations of two registered poplar clones. Environmental Pollution. 2008; 153: 137–147.'},{id:"B105",body:'Wang C, Zhang SH, Wang PF, Hou J, Zhang WJ, Li W, Lin ZP. The effect of excess Zn on mineral nutrition and antioxidative response in rapeseed seedlings. Chemosphere. 2009; 75: 1468–1476.'},{id:"B106",body:'Shi Q, Zhu Z. Effects of exogenous salicylic acid on manganese toxicity, element contents and antioxidative system in cucumber. Environmental and Experimental Botany. 2008; 63: 317–326.'},{id:"B107",body:'Benavides MP, Susana MG, María LT. Cadmium toxicity in plants. Brazilian Journal of Plant Physiology. 2005; 17(1): 21–34.'},{id:"B108",body:'Sandalio LM, Dalurzo HC, Gomez M, Romero-Puertas MC, Del-Rio LA. Cadmium-induced changes in the growth and oxidative metabolism of pea plants. Journal of Experimental Botany. 2001; 52(364): 2115–2126.'},{id:"B109",body:'Ekmekci Y, Tanyolac D, Ayhan B. Effects of cadmium on antioxidant enzyme and photosynthetic activities in leaves of two maize cultivars. Journal of Plant Physiology. 2008; 165: 600–611.'},{id:"B110",body:'Schützendübel A, Polle A. Plant responses to abiotic stresses: Heavy metal-induced oxidative stress and protection by mycorrhization. Journal of Experimental Botany. 2002; 53(372): 1351–1365.'},{id:"B111",body:'Dong J, Wu F, Zhang G. Influence of cadmium on antioxidant capacity and four microelement concentrations in tomato seedlings (Lycopersicon esculentum). Chemosphere. 2006; 64: 1659–1666.'},{id:"B112",body:'Tanyolac¸ D, Ekmekci Y, Unalan S. Changes in photochemical and antioxidant enzyme activities in maize (Zea mays L.) leaves exposed to excess copper. Chemosphere. 2007; 67: 89–98.'},{id:"B113",body:'Zhao S, Zhang Y, Ye X, Zhang Q, Xiao W. Responses to cadmium stress in two tomato genotypes differing in heavy metal accumulation. Turkish Journal of Botany. 2015; 39.'},{id:"B114",body:'Tan M, Zhang XY, Fu QS, He ZQ, Wang HS. Effects of grafting on physiological characteristics of melon (Cucumis melo) seedlings under copper stress. The Journal of Applied Ecology. 2014; 25(12): 3563–3572.'},{id:"B115",body:'Kıran S, Özkay F, Kuşvuran Ş, Ellialtıoğlu Ş. The effect of humic acid applied to the plants of lettuce (Lactuca sativa var. crispa) irrigated with water with high content of lead on some characteristics. Research Journal of Biological Sciences. 2014; 7(1): 14–19.'},{id:"B116",body:'Sharma P, Dubey RS. Lead toxicity in plants. Brazilian Journal of Plant Physiology. 2005; 17: 35–52.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Sebnem Kusvuran",address:"skusvuran@gmail.com",affiliation:'
Cankiri Karatekin University, Kizilirmak Vocational High School, Cankiri, Turkey
Department of Horticulture, Faculty of Agriculture, Ankara University, Ankara, Turkey
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\n
1. Introduction
\n
Citrus fruit are grown commercially in more than 50 countries around the world and are major commodities in the international trade [1, 2]. In Europe, the exceptional characteristics met by some of these produces have granted them the Protected Geographical Indication (PGI), such as the lemons (Citrus limon (L.) Osbeck) of Menton in France, Sorrento, Amalfi and Syracuse, and the Sicilian blood orange (Citrus × sinensis) in Italy, the “Algarve Citrus” in Portugal, or the “Valencianos Citrus” in Spain.
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As non-climacteric, citrus fruit are only harvested at their optimal edible ripening stage, and are required to meet the expectations of the current consumer who demands for fruit not only with the best appearance, flavor, and nutritional properties, but that also comply with safety, traceability, and the sustainability of the cultural practices used. Like any other commodity, citrus fruit are subjected to worldwide standard specifications within the value chain [3] on their quality attributes (QA). Additionally, there are also adjustments to these requirements on quality and commercial ripening indices, that arise from the respective PGI normative of each commodity, growing regions and destination markets [4]. The main external quality attributes (EQA) accounted for citrus fruit are general appearance, size, weight, and color. Among the internal quality attributes (IQA), soluble solids content (SSC), titratable acidity (TA), juiciness, maturity index (MI; MI = SSC/TA), and the absence of internal defects are the most relevant. Although firmness is not defined quantitatively, it represents an important IQA, since it is a limiting factor regarding postharvest handling, transport and shelf-life, fruit being expected to maintain a good consistency through the whole supply chain.
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Once fruit attain the expected IQA, additional factors will condition the harvest of citrus fruit: orchard yield and size, ripening variability, harvest cost, storage conditions, market prices and consumers’ demand. Although dependent on the country, producers are provided with three options to handle these constrains: (i) immediate harvest and marketing; (ii) immediate harvest and cold-storage; or (iii) delayed fruit harvest. Opting for immediate harvest may result in minimal organoleptic quality and low prices, whereas postponing it until favorable market conditions, risks fruit drop, decay and spoilage caused by extreme weather events, pests and diseases [5]. To prevent some of of these consequences, producers resort to the regular use of pesticides, which increase the production costs and impact negatively the environment [6, 7]. Cold-storage is used in some of the major citrus producing countries, such as Spain or South Africa, and require very strict conditions to avoid fruit loss caused by chilling and/or freezing injury [8]. Both, cold-storage and harvest delay may lead to adverse alterations in the citrus-like flavor, and thus fruit quality deterioration, even if MI or SSC remains acceptable for marketing [9]. In all cases, fruit become more susceptible to the occurrence of physiological disorders that cause internal and/or external defects. Among the most typical physiological disorders registered through the supply chain of citrus fruit, there is the section drying, the rind breaking disorder (RBD), the rind pitting disorder (RP), freezing damage, and granulation, as reported for tangerine (Citrus tangerine Tanaka [10], ‘Nules Clementine’ mandarin (Citrus × clementina) [11], ‘Marsh’ grapefruit (Citrus × paradisi Macfad.) [12], sweet lemons (Citrus limettioides Tan.) [13], and ‘Honey’ pomelo (Citrus maxima Merr.) [14], respectively. These disorders are difficult to sort out by visual inspection at harvest, but lead to posterior fruit deterioration, limiting their quality, shelf-life, price and acceptance by consumers. In fact, there are strict standards for fruit sorting and grading, which require the detection of some of these disorders, throughout the supply chain, as established by the California Department of Food and Agriculture (CDFA). For exemple, it is not permitted to sell oranges (Citrus sinensis (L.) Osbeck) if, generally, more than 15% of fruits per batch have considerable freezing damage [15].
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Therefore, the ripening of citrus fruit at harvest is a major determinant of their final quality after the whole postharvest handling processes, the occurrence of storage disorders, and the produce shelf-life span [16]. It also affects the rate of fruit loss between the tree and the consumers’ home. Thus, the management and the decision capacity of the optimal harvest date (OHD) is a critical step in the supply chain. The current approach followed by producers and packinghouses to establish it and therefore, to decide on the harvest, is to collect small fruit sets from the various orchards by the beginning of each variety harvest season, and to use them to determine QA through standard methods, that in most cases are destructive, subjective and very time-consuming.
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However, all QA vary greatly inside the same orchard, either in terms of absolute values and/or in terms of spatial and temporal distribution, and even in the same tree. This has been shown in citrus orchards of ‘Shiranuhi’ mandarin (C. unshiu × C. sinensis) × C. reticulata [17], ‘Ortanique’ (Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) [18], mandarin (Citrus reticulata Blanco) [19], and ‘Newhall’ and ‘Valencia Late’ orange [20]. Multiple factors, such as the level of sunlight exposure and the associated fruit temperature on the tree, fruit yield and size, tree vigor and age, rootstocks, site-specific nutritional requirements and micro topographies within the orchard, are reportedly associated to this variability [21, 22, 23, 24]. Furthermore, the location of the orchards and their edaphoclimatic conditions, as well as the cultural practices also induce variability on the fruit maturation process, leading to different levels of QA and different ripening rates observed for the same cultivar at different sites [20, 21]. Consequently, the number of tested fruits with the standard methods is seldom statistically representative of the orchard, leading to the sub-representation of the effective ripening stage of the fruit within and between orchards, which results in a limited assessment of their ripening, heterogeneous fruit quality, a deficient OHD management and a weak traceability in the citrus supply chain [25, 26, 27].
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Overall, there is the need to upgrade the management and the sustainability of citrus fruit supply chain with smart and nondestructive technologies that allow a fast, objective, accurate and extensive assessment of fruit QA and ripening on-tree and in the following postharvest, to replace conventional methods. Their aim would be to deliver the best produce to the markets, and contribute to reduce the current level of food loss around the globe, that involves a large portion of fruit and vegetables [28, 29, 30]. Considering how much of the world’s population lacks food security, and the importance of these commodities in the provision of essential nutrients and vitamins, which could prevent malnutrition, that kind of technologies would comply with the sustainable development goals (SDGs) proposed by the Food and Agriculture Organization (FAO), International Fund for Agricultural Development (IFAD), and the World Food Programme (WFP), in the 2030 Sustainable Development Agenda, which supports a global commitment to end poverty, hunger and malnutrition by 2030, creating a #ZeroHunger world [31, 32].
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The large number of reports published in the past two decades, show an active, and highly motivated research concerning the development of various nondestructive technologies for the assessment of quality and ripening parameters of a wide variety of fruit, including citrus [16, 33, 34, 35, 36, 37]. These techniques are used on inline sorting systems, on the bench or in the field and come in many forms, prices and commercial brands. Among them, the visible–near infrared reflectance spectroscopy (Vis–NIRS), is conceivably one of the most suitable and advanced nondestructive technologies currently used to monitoring several horticultural produces. It has been implemented in applications ranging from the inline automated grading systems, assessing up to 10–12 fruit per second, to handheld units suitable for field use, operating in full sunlight and varying ambient temperature [38, 39]. Additionally, it continues to grow stronger as a major investigation topic worldwide, with a major potential for improvement and contribution to the state of the art of precision agriculture and agronomic systems management [40].
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This chapter comprises a brief explanation of Vis-NIRS fundamentals and a review of the various reports on its application published since 2012. Reports published before 2012 were already covered in the last review by [41] and will not be repeated here, with a few exceptions that represent relevant breakthroughs in the area. It will further attempt a critical evaluation on the limiting issues that need further research, to implement it as an effective nondestructive method to assess these commodities’ quality and optimal ripening.
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The authors invite the reader to complement this chapter with some of the most outstanding reviews published throughout the years, by the main researchers working on the subject (but not only in citrus). These reviews comprise the principles of the technique, its various methods and the listing of fruit and the respective QA for which it has provided calibration models [41, 42, 43, 44, 45], the overview on the publications and main research groups in the field [40], various recommendations for future research activity in the area regarding the adequate experimental design and the reporting requirements [38], as well as the current real-life applications available on the market that seem to comply with the warranted robustness for the technology to be integrated in the supply chain of many crops, including citrus [38, 39].
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2. Fundamentals of visible–near infrared reflectance spectroscopy (Vis–NIRS)
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In this review we will adopt the most common definition that Vis–NIRS covers the wavelength range 400–2500 nm of the electromagnetic spectrum. The lower limit is consensual, since it is the onset of the visible range, but the upper limit is mainly defined by the spectral response of the most common spectrometers. It comprises the visible (Vis) region (400–750 nm), the more penetrative short wave NIR (SWNIR), or Herschel region (750–1100 nm), and the near infrared region (750–2500 nm) of the spectrum [38, 39, 45]. The NIR radiation was discovered by Friedrich Wilhelm Herschel in 1800, and was first used in agricultural applications to measure the moisture in grain in the late 1960s [45]. The first Vis–NIRS application was commercialized in Japan in 1989 to sort peaches based on SSC in an automated grading line, but the research on its principles, applications and on the development of new customized systems, have only followed some decades later, being quite active nowadays [38, 39, 40].
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2.1 Interaction of radiation with the fruit
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When a light beam from the sun or a tungsten lamp, hits a fruit or any other sample, the incident radiation may be specularly reflected, absorbed or transmitted, and the relative contribution of each phenomenon depends on the chemical constitution and physical parameters of the sample (Figure 1) [46]. The spectral distribution of the radiation that penetrates the product change through wavelength dependent scattering and absorption processes. The photons that enter the fruit may emerge through multiple scattering in the tissue. Light emerging on the same side of incidence is described as diffuse reflection, while light emerging on the opposite side is described as diffuse transmition. Both diffuse modes may be understood in a general sense as ‘transmitted’, according to the initial description. The emerging diffuse light is collected by a spectrometer, originating the term diffuse reflection spectroscopy. The spectral features depend on the chemical composition of the product, as well as on its light scattering properties which are related to the sample microstructure. Fruit and vegetables are turbid media, in which scattering events dominate over absorption in the visible (400–750 nm), and particularly in the SWNIR and NIR ranges of the electromagnetic spectrum (750–2500 nm) [44] (see Figure 1).
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Figure 1.
When light hits a fruit, the photons may be reflected at the fruit surface (specular reflection) or enter the fruit tissue. In the latter case, a succession of scattering events takes place, where the photons change direction. Some of them reemerge, originating diffuse reflection (or transmission, not represented), and the other are eventually absorbed.
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In thin rind fruit most of light interaction takes place on the flesh and the skin has mainly a modulation effect upon the spectra. In most citrus, however, most of light interaction occurs in the thick rind and few photons probe the flesh. Thus, the assessment of IQA depends on the interplay between pulp and skin biochemistry and their optical properties [47, 48, 49].
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Absolute quantification of diffuse reflected light (for example, as spectral radiance [W\n\n⋅\n\nsr−1 m−2 Hz−1]) is of little use, because it depends obviously on the characteristics of the light source. The calculation of reflectance avoids that subjectivity, since it normalizes the absolute measurement of the sample’s reflection by that of a reference material, usually a near perfect reflector (‘white’) in the wavelength range under study. It should be stressed, however, that even the reflectance depends on the collection geometry (solid angle of collection, viewed area, etc.). Common choices for the reference material include Spectralon or Teflon, with nearly 100% reflection in the Vis-NIR. Reflectance \n\nR\n\nλ\n\n\n is calculated according to the Eq. (1) presented below.
where \n\nS\n\n stands for the Sample counts, \n\nD\n\n for the Dark counts, \n\nRef\n\n for the Reference counts and \n\nλ\n\n is the wavelength. Here, counts refer to the digitized output of the spectrometer, which are proportional to the spectral radiance measured in a specific geometry. The dark counts are obtained with the spectrometer closed and represent the electronic noise, which must be subtracted from the sample and reference measurements.
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2.2 ‘Point’ and imaging measurements
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Vis-NIRS is most commonly applied on specific ‘points’ of the fruit, by observing a small area, which produce an average spectrum for that specific site. This is called point measurements. But Vis-NIRS can also be applied on extensive sections across the fruit, through multispectral and hyperspectral measurements, which create an image of the measured sections for each wavelength band [44]. The main difference between multi- and hyperspectral modes are the number of wavebands used. Multispectral imaging uses a set of filters and a common digital camera to deliver typically no more than ten bands, while hyperspectral cameras merge imaging and spectral separation in the optical hardware to produce hundreds of contiguous wavebands. Another way to look into hyperspectral images is to think that it yields the reflectance spectrum for each spatial position of a sample (i.e., for each pixel of the image) [44]. Both techniques, although costly, have been shown to successfully assess several IQA, diseases and defects in several fruit, including citrus fruit [50, 51]. Yet, extensive investigation is needed to allow both the acquisition and image processing software to be implemented in real-time systems. Thus, this chapter will only address the systems that perform ‘point’ measurements, based on their much wider spread, cost-effective and friendly use through the supply chain, and particularly under field conditions. Further information on the principles and applications of multispectral and hyperspectral Vis-NIRS technologies could be found in the reviews by [44, 50].
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2.3 Instrumentation and measurement setup
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There are currently, a large variety of both commercial and lab-made customized Vis-NIRS systems, with various shapes, sizes, and prices, that operate in many spectral ranges, and have reportedly allowed the assessment of several QA, the content of critical compounds and the diagnostic and/or prediction of disorders in a wide sort of fruits, including citrus (Tables 2–4). Nevertheless, most of the current commercial fruit applications of Vis-NIRS are based on the use of silicon-based spectrophotometers comprising the Vis-SWNIR region (400–1100 nm), because of their accessible prices and the larger light penetration depth in this band, in comparison to the significantly more expensive InGaAs-based devices (900–2500 nm), that do not add too much value to the quality assessment procedure [39].
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From handheld, benchtop, to inline automated grading system, all Vis-NIRS devices comprehend the following fundamental components: an optical spectrometer, a light source (usually a tungsten halogen light bulb) and collection optics (optical fibers, lenses, integration spheres, dedicated probes). The current spectrometers typically include a connection for an optical fiber, an entrance slit (that defines the spectral resolution), a diffraction grating to separate the light into its spectral components, mirrors for collimation and focusing, and a light sensing device that is usually a one-dimensional CCD (Charge-Coupled Device) or CMOS (Complementary Metal-Oxide Semiconductor).
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The Vis-NIR spectra may be acquired according to three principal geometrical configurations, as depicted in Figure 2: the reflectance mode (a), the transmittance mode (b), and in the interactance mode (c). The reflectance mode is susceptible to receive specularly reflected light, which may be a disadvantage, since only a fraction of the collected photons probes the fruit interior. In the transmittance mode the photons probe necessarily the fruit interior; however, the optical signal may be weak and noisy. The interactance mode is a tradeoff between the two previous modes: by using a contact probe it avoids specularly reflected photons and receives only those traveling through the fruit flesh. Also, the distance between light injection and collection is small, insuring a good optical signal. However, this is also a disadvantage, since the probing depth into the fruit pulp is shallow.
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Figure 2.
Setup for the acquisition of Vis-NIR reflectance spectra in (A) reflectance, (B) transmittance, and (C) interactance modes. Based on [45].
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The choice of the geometry is thus of the utmost importance for obtaining good results, and should account for the fruit and the assessed QA. The penetration of NIR radiation into fruit tissue decreases exponentially with the depth, which is quite critical in thick rind fruit such as citrus [50]. Furthermore, the choice of the detection mode might be influenced by the spectral range used, as report by [48], in which both interactance and reflectance modes produced similar models to assess the SSC of ‘Sunkist’ navel oranges in the Vis-SWNIR range, but the participation of Vis region degraded this assessment in the transmittance mode. in general, to detect the internal defects, the transmittance mode should be chosen, while the other two modes are quite reliable regarding other IQA (Tables 2–4).
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2.4 Typical Vis-NIRS spectrum and its interpretation
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All studies on the use of Vis-NIRS to assess the fruit QA start by acquiring the reflectance (R) spectra, which is then converted to the respective absorbance log(1/R) spectra (Figure 3). The main spectral differences observed in a wide variety of fruit are in the visible region, namely in the 400–750 nm range. This is due to changes in the pigments’ content through ripening, namely chlorophylls, carotenoids and anthocyanins present on the fruit rind [38]. In fruit that change from green to yellow/orange/red colors through ripening, the spectral information on the pigments’ absorption range, may provide accessory indirect correlations with IQA such as firmness, as found in ‘Rocha’ pear (Pyrus communis L.) [77]. This, however, is not so clear in citrus fruit because their color change do not correlate with their maturity and depends on the orchards’ location climate [6]. Otherwise, the pattern of the absorption spectra in the NIR range is quite similar among the various fruit species, although position and magnitude of the peaks are fruit specific, even among citrus fruit varieties [41]. The magnitude of the peaks and minima are also dependent on the acquisition mode used, but in general the same features are present and the landscape of the spectra is similar among the same fruit as reported for ‘Sunkist’ oranges [48].
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Figure 3.
(a) Average reflectance spectra of a set of 255 ‘Valencia Late’ oranges and 239 ‘Rocha’ pears acquired in the Vis/NIR range in the interactance mode; (b) Average absorbance spectra of the same set of fruit. The nominal positions of the most important absorption bands are indicated in the curves. The number is the order of the transition, \n\nν\n\n stands for stretching vibration, \n\nδ\n\n for bending vibrations and the sum indicates combination bands (for example, \n\n3\nν\n\n+\n\nδ\n\n(O–H) represents the combination band of the second overtone of stretching with the fundamental bending in O–H); (c) Savitzky–Golay [76] filter of second derivative order applied to the absorbances. The bands are again indicated; (d) to (f) Same as in (a) to (c) but in the NIR range, with the spectra acquired in reflectance mode.
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The spectra in the NIR range convey information mainly related with vibrational bands (stretching and bending) of the relevant functional organic groups, such as O–H, C–H, C–O and C=O. The compilation of the main wavebands present in citrus fruit, namely, O-H and C-H vibration absorptions are presented graphically in Figure 3. These groups exist in all fruit organic molecules, but variations associated with water and storage reserves may induce slight changes in the spectra that may be related with the IQA. Vibration states are quantized and the transitions between states are said to be fundamental or overtones. The fundamental transitions (corresponding to a fundamental band) refer to the transition from the ground state to the first excited state, and take place mainly in the infrared range, that is above 2500 nm. In this range, the absorption peaks are distinguishable and correlate directly to specific compounds, allowing a better assessment of organic compounds such as vitamin C, citric acid or sucrose, as reported in ‘Valencia’ orange [74]. In contrast, the overtone bands correspond to transitions to higher excited states, with a large number falling in the NIR range. For example, the first overtone band corresponds to the transition from the ground state to the second excited state. A very crude approximation is that the n-th overtone frequency is close to (n-1) times the fundamental frequency. Thus, a general rule is that overtones have higher frequencies and lower amplitudes than the fundamental. For example, the fundamental frequency for O–H stretching (\n\n1\nν\n\n) is around 2700 nm, which means that is beyond the range of the most common NIR dispersive-type spectrometers. However, the overtones are within their instrumental range: \n\n2\nν\n\n at 1420 nm (first overtone, strong intensity band), \n\n3\nν\n\n at 970 nm (2nd overtone, medium intensity) and \n\n4\nν\n\n at 750 nm (3rd overtone, low/very low intensity band). The quoted values are only indicative of a typical band central value. Indeed, the vibrations are dependent on the chemical environment, which results in a frequency spread of the bands. Finally, it is important to refer the combination bands. These correspond to the superposition of vibration motions. For example, the fundamental bending mode (\n\n1\nδ\n\n) of water at 6300 nm (infrared range) may combine with the fundamental stretching mode at 2700 nm (\n\n1\nν\n\n), to generate a combination band around 1900 nm [\n\nν\n\n+\n\nδ\n\n (O–H)]. Having in mind that the fruit tissue is composed by many different organic molecules, it is easy to understand that the spectral landscape of fruit NIR reflectance is a continuum, due to band superposition, as previously shown by [74], when comparing NIR and medium infrared spectroscopies (MIR), to assess several compounds in ‘Valencia’ oranges. Summarizing, the NIR spectra of a fruit contains mainly overtones and combination bands of stretching and bending vibrations of the main functional organic groups of relevant organic compounds regarding the fruit IQA, such as O–H and C–H. The large number of possible vibrations and corresponding bands originates a spectral landscape with very broad and unspecific features, from which it is nevertheless possible to retrieve useful information. For instance, [74] obtained better prediction of fructose and reducing sugars when using NIRS than MIRS.
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The typical Vis–NIR and NIR reflectance and absorption spectra of ‘Valencia Late’ orange and ‘Rocha’ pear are depicted in Figure 3 [78]. Both fruit spectra were relatively flat from 700 to 910, followed by strong water absorption peaks around 970, 1450 and 1940 nm. However, the C-H bands may distort slightly the water peaks, and the analysis of this distortion conveys more information than the main peaks alone. It is from these patterns associated with the OH and CH vibrations that it is possible to retrieve the information about sugars. Even the water bands by themselves may convey information about the sugars, because the concentration of sugars and water are interdependent [41, 66, 79, 80].
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In the Vis–NIR range the most prominent feature is the \n\n3\nν\n\n(O–H) peak at \n\n∼\n\n 975 nm. This peak is reported in the literature in the range 960–980 nm [79], but the actual location depends on multiple factors: (i) the degree of OH bonding; (ii) the temperature; (iii) the presence of other close bands. In other words, within different chemical environments, the OH group will peak at different wavelengths. Effect of (iii) is more clearly observed in Figure 3c. Indeed, the smooth 975 nm peak observed in absorbance has a more fine structure disclosed upon derivation. Thus, the peak \n\n4\nν\n\n(CH2) at 930 nm is actually coalescing with the main water peak, causing a depression in the 2nd derivative left positive peak. The form of this overlap is a source of information about all organic compounds content, namely sugars, acids, proteins, etc.
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A minor feature, but consistently observed in most fruit, is the slight inflection around 840 nm, which is caused by the band \n\n3\nν\n\n+\n\nδ\n\n(O–H). In this case it is more clearly observable in the reflectance spectrum, Figure 3a. In this discussion is important to have in mind that second derivation of a symmetric peaks yields a negative peak at the same position, with two lateral smaller positive peaks. This is clearly observed for the 840 nm feature in the 2nd derivative plot, with the negative peak coinciding with the \n\n3\nν\n\n+\n\nδ\n\n(O–H) absorption wavelength. On the contrary, the peaks of the \n\n3\nν\n\n(O–H) features do not coincide in the absorbance and 2nd derivative plots, which clearly indicates spectral overlap.
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Similar curves are observed in other cultivars. For example, in ‘Newhall’ orange [66] the same structure for the second derivative plot was observed, although a different technique was used, namely the Norris derivative [81].
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Concerning the NIR spectra, those from the oranges show three main peaks at 1190, 1450 and 1940 nm, whose origin may be traced to the \n\n3\nν\n\n(C–H), \n\n2\nν\n\n(O–H) and \n\nν\n\n+\n\nδ\n\n(O–H) bands, respectively. However, satellite bands overlap, as in the Vis/NIR case. The most ‘pure’ peak is the first, around 1200 nm, corresponding to the \n\n3\nν\n\n(C–H) band. As is the 840 nm band, absorbance and 2nd derivative peaks coincide. The other two main peaks are more complex blends of two or more bands. For example, the second peak around 1450 nm, although dominated by the stronger \n\n2\nν\n\n(O–H) band, has contributions from \n\n2\nν\n\n+\n\nδ\n\n(C–H) and \n\n2\nν\n\n+\n\n2\nδ\n\n(C–H). Consequently, the 2nd derivative feature associated with this mix is more complex. The same could be said about the third peak.
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Furthermore, due to several causes, the various peaks, even when they coincide among different fruit, may present different levels of importance, signified by their infrared values, with the various IQA. For instance, the combination band of OH reported at 839 nm correlated highly with SSC in ‘Rocha’ pear samples, but not in ‘Valencia Late’ oranges [78].
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3. Chemometrics
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As it has been mentioned earlier, Vis–NIR spectra of fresh fruits tend to be composed by a large superposition of absorption bands. The presence of a substantial amount of water in fresh fruit has a big impact in the spectra, dominating most of the spectral landscape. Therefore, the signals corresponding to the absorption bands of key chemical compounds such as sugars and acids, become masked by water and are only discernible as weak fluctuations in the spectrum. Given the complex interplay between the multiple absorption bands and their weak amplitudes, most of the times the linear relation between chemical compound concentration and absorption (Lambert–Beer law) is almost lost. In order to be able to extract information about chemical concentrations from this type of spectrum, we have to look for relationships’ patterns between different wavelengths. This is done resorting to multivariate statistical techniques, that when applied in the field of analytical chemistry is called Chemometrics. This research field can be considered as a subset of the broader area of Machine Learning, and pursues the same goals, i.e., infer critical information from high dimensional data. There is a vast literature on this subject for those who wish to learn more about Chemometrics. Here are some suggestions for introductory and advanced levels [82, 83, 84, 85, 86]. In this section, it is presented a brief introduction to the scientific language used in this area, with the sole objective of familiarize the reader with the main concepts that are often presented in the literature. In Vis-NIRS of fresh fruit, the input data are spectra, i.e. one-dimensional arrays of values, each one corresponding to the intensity of light (diffusively reflected or absorbed) at a specific wavelength. Each spectrum \n\n\nX\nn\n\n\n (\n\nn\n=\n1\n,\n…\n\n number of samples) represents a measurement or sample and each point \n\n\nx\ni\n\n\n (\n\ni\n=\n1\n,\n…\n\n, number of variables) of the spectrum is usually referred to as input variables, spectral features or simply as ‘wavelengths’. The macroscopic properties or QA features that are obtained through laboratory testing (e.g. SSC, firmness, etc.) are commonly defined as target variables \n\n\nY\nn\n\n\n or simply attributes. Chemometrics consists on the application of mathematical/statistical methods that allow mapping the spectral features \n\n\nx\ni\n\n\n into the target variables \n\nY\n\n. These methods can be subdivided into two broad subcategories: unsupervised and supervised. In the former type of method, only the input variables \n\n\nx\ni\n\n\n are used and, the main purpose is to find, for example, trends within the data, clusters that can be used for classification or other general characteristics of the data set. On the other hand, supervised methods use both input and target variables and can be used for classification tasks (e.g. discriminate between different fruit sub-species or origins) or for quantitative (regression) prediction of attributes that have continuous distributions (e.g. SSC, firmness, TA, etc.).
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3.1 Spectral pre-processing and outlier detection
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In order to implement the best calibration model possible to predict the expected QA, often the spectral data has to be preprocessed before being used. Preprocessing techniques are used to remove irrelevant information (noise, systematic errors and faulty samples) that can degrade the performance of the numerical algorithm used to develop the calibration model. Several preprocessing methods have been created for this purpose and reviews, such as [83, 87], present a wider scope of these techniques. A brief summary of the most commonly used methods is presented in Table 1. For Vis-NIRS, the most common forms of spectral preprocessing can be stacked into two groups: scatter corrections (SC) and derivative techniques (DT). Scatter-corrective methods are used to remove the influence of scattered light that can contaminate the diffuse reflectance spectra. The rationale behind SC techniques is to remove effects that are unrelated to the chemical composition of the samples and that just depend on the measurement geometry or samples morphology. On the other hand, DT are designed to improve signal to noise ratios, eliminate systematic baseline biases and enhance spectral variations. Another type of preprocessing commonly mentioned in the literature is outlier detection. This process consists in identifying and removing from the data set, samples that are very different from the rest of the samples. These outliers can be for example, reflectance spectra that were defectively acquired or fruit with odd properties. The idea is to remove these samples from the data set, using some pre-defined metric in order to feed the model only with the most representative samples in the data set that lead to a correct mapping of the attribute being predicted by the calibration model constructed.
Each spectrum \n\n\nX\nn\n\n\n is regressed against a reference spectrum (usually the mean spectrum, \n\n\nX\nm\n\n\n) using a least square method. Then, the corrected spectrum is computed based on these regression coefficients. There are more sophisticated variants of this method (e.g. Extended MSC [89] that try to correct for additional additive effects).
Each individual spectrum \n\n\nX\nn\n\n\n is normalized to have zero mean and unit variance. In some cases, this can also appear as sample standardization.
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Smooth derivatives (Savitzky–Golay (SG) [76] and Norris-Williams (NW) [81]
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The spectrum derivative in relation to the wavelength is computed. Usually the first derivatives remove baseline effects and the second derivative remove baseline and linear trends in the spectrum. Smooth derivatives are computed using an averaging multi point window in order to be more robust against spurious noise. SG and NW algorithms are the most common methods to compute these derivatives.
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De-trending
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This process consists in fitting a polynomial (usually 1st or 2nd order) to the spectrum and subtract it from the signal. This provides baseline correction.
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Scaling
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Scaling is a sort of umbrella under with we can find multiple types of data manipulations. The most common ones are: baseline subtraction, sub-sampling, normalization on columns (all spectral features in the data set are scaled between a max and min values) and normalization on rows (the individual spectral features are scaled between min and max).
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Table 1.
Most common spectral preprocessing techniques.
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3.2 Clustering
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In Vis–NIR spectra of fresh fruit, the common spectrum is often described by smooth mounds and soft depressions. This means that adjacent wavelengths can be highly correlated. Therefore, in order to reduce redundancy of information provided by neighbour features \n\n\n\nx\ni\n\n\nx\n\ni\n+\n1\n\n\n\nx\n\ni\n+\n2\n\n\n…\n\n\n (also called co-linearity), sometimes it is beneficial to restrict the number of used input variables. This is often called dimensionality reduction and is very important for the right operation of certain calibration models. The simplest way to deal with this problem is by using sub-sampling, where a certain number of spectral features are discarded, e.g. every 3rd or 5th point in the spectra. To deal with this problem of dimensionality reduction, some ‘clever’ algorithms were introduced, the most common being the Principal Component Analysis (PCA) algorithm, the Hierarchical Clustering Analysis (HCA) and K-Means. the latest two methods can be used for classification tasks (mapping a cluster to a class) and for outlier detection as well. If samples are too far apart from the defined clusters (according to some metric such as the Euclidean distance or the Mahalanobis distance), then this suggests that it might be an outlier.
\n
\n
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3.3 Classification and regression models
\n
As we mentioned earlier, depending on the problem at hand, we might need to implement a classification or a regression model for our data. Multiple Linear Regression (MLR) is perhaps one of the most straight forward methods to implement. It expands the application of simple linear regression to the multivariate case by linearly combining them. Due to its simplicity, this method has some drawbacks, namely an inefficient applicability in the cases of high co-linearity in the data, and when the number of features in the data set is higher than the number of samples. This is often the case of fresh fruit Vis–NIRS datasets and hence its applicability has been limited. One way of overcoming these limitations, is to use a dimensionality reduction method, such as PCA and then perform MLR on these lower dimensional components. This workflow is known as Principal Component Regression (PCR). Partial Least Square Regression (PLS) is without question the most widely used method to create calibration models to predict the most QA of fresh fruit (Tables 2–4). As opposed to PCA, the PLS algorithm takes into account the covariance between input \n\n\nx\ni\n\n\n and target \n\n\nY\ni\n\n\n variables. In the same spirit as PCA, PLS also projects the data into a latent space, but this time the components are defined along the direction of maximum variance between \n\n\nx\ni\n\n\n and \n\n\nY\ni\n\n\n. These components are called latent variables (also named factors by some researchers), are built in order to model the target variable, and their number is what defines the quality of the PLS model. In general, a low number of latent variables usually lead to more robust predictions, but that might not always be the case. A variant of PLS named PLS Discriminant Analysis (PLS-DA) can be used to deal with classification scenarios when the target variables \n\n\nY\ni\n\n\n are not continuous (e.g. 0, 1 for fruits without and with defects). The models mentioned so far are can be described as linear because they rely on a linear combination of multivariate solutions. Besides the easiness of implementation, they are also classically appreciated in Chemometrics because they are easy to interpret in terms of feature importance, i.e., after fitting the model to the data we can back-trace some parameters (e.g. regression coefficients) and find what wavelengths or spectral bands better contributed to the prediction. In turn, this allows inferring information about the chemical concentrations and can be used to identify biological and metabolic behaviors.
Overview of applications of Vis-NIRS to measure the QA of citrus fruit by portable spectrometers/systems under laboratory conditions. List of symbols in Table 5.
Selected wavelengths in this range.
Selected wavelength bands in this range.
Cross validation.
Combinations of cal. and val. Sets.
Calibration.
Combinations of cal. and val. sets (harvest day).
Combinations of cal. and val. sets (orchard location).
Overview of applications of Vis-NIRS to measure the quality attributes of citrus fruit by portable systems on-tree. List of symbols in Table 5.
Corrected to bias.
\n
\n
\n
\n\n
\n
Colums
\n
Rows’ content
\n
\n\n\n
\n
IQA: Internal Quality Attributes
\n
\n\n\nβ\n\n Car: \n\nβ\n\n carotene; BrimA: Brix minus Acids (SSC-k(TA)); Chl a: chlorophyll a; Chl b: chlorophyll b; CitAc: Citric Acid; DegOleoc: Degree of oleocellosis; DMC: Dry Matter Content; DPPH: 2,2-diphenyl-1-picrylnhydrazyl; DM: dry matter; Firm: Firmness; Fruct: Fructose; Gluc: Glucose; Granul: Granulation; JuiceVol: Juice Volume; MPF: Maximum Penetration Force; MI: Maturation Index; Pectin: Pectin Content; PerTh: Pericarp Thickness; RateOleoc: Rate of oleocellosis; RBD: Rind Breakdown disorder; RedSug: Reduced Sugars; RindPitt: Rind Pitting; SSC: Soluble Solid Content; Suc: Sucrose; TA: Titratable Acidity; TAO: total antioxidant capacity; TotCar: Total Carotenoids; TotSug: total sugars; TPh: Total Phenolic content; VitC: Vitamin C; WC: Water Content; WL: Weight Loss
\n
\n
\n
Mode: mode of acquisition
\n
R (Reflectance), I (interactance) or T (transmittance)
\n
\n
\n
StatMethod: Statistical method employed
\n
iPLS: Interval PLS; LAR: Least Angle Regression; LOCAL: Patented algorithm for PLS regression from a subset of proximal calibration samples; LS-SVM: Least squares support vector machines; MLR: Multiple linear regression; MPLS: Modified PLS PCA-NN: Principal components analysis combined with artificial neural networks; PCA-BPNN: back propagation neural network (BPNN) based on principal component analysis (PCA) PLS: Partial Least Squares; PPSOPLS: piecewise particle swarm optimization (PPSO) algorithm based PLS SIMCA: Soft independent modelling of class analogy; SPAPLS: Succesive Projections algorithm coupled with PLS; SVM: Support vector machines; VABPLS: variable adaptive boosting partial least squares
\n
\n
\n
Val: Model validation type
\n
I (internal), CV (cross validation only), E (external)
In the last couple of decades, non-linear models imported from other areas of Machine Learning have begun to permeate Chemometrics, and given its high use case in the literature, Support Vector Machines (SVM) is one of the most popular. The strategy of this model consists in searching for boundaries that separate two cluster or classes. The algorithm tries to find the best boundary between classes by maximizing a distance margin between neighbor samples. It has the advantage that it can use kernel tricks to transform the data points into another mathematical space, where these boundaries are easier to establish. SVMs were initially used for classification tasks, but have been extended to deal with regression problems as well (SVR). SVR has been used successfully for many datasets, and the most often mentioned drawback is the complexity of its optimization task. Another popular type of non-linear models that is often used for classification and regression problems is Neural Networks (NN). These represent a wide class of algorithms with many types of architectures and are derived from the field of Artificial Intelligence. In recent years, classical NN architectures such as the Multi-Layer Perceptron has been increasingly substituted by more modern architectures developed for Deep Learning. The most promising of these NN are the so called Convolutional Neural Networks that have been very successful in image recognition tasks.
\n
\n
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3.4 The quality of a calibration model
\n
Independent of the type of model that is used for prediction or classification, the important thing is to find how well it performs on the desired data. To assess the quality of the predictions made by the calibration models, several metrics are often used. In a recent review by [38], the author makes a case for the uniformization of the report of error metrics in future publications. In what follows these recommendations are highligted. The partitioning of the data for model development is very important. The data set should always be split into two sub-sets, called train and test sets. The train set, as its name suggests, is used for the calibration model development and, once the main hyper-parameters have been established, the model is used to predict the test set and assess its performance. Model development can be done with the full train set using a cross-validation strategy or by further splitting it into calibration and tuning (or assessment or validation) sets. As a note of caution, it is important to mention that for different areas of Machine Learning the names given to these data splits can vary and that can lead to some confusion. Otherwise, the test set should be derived from a different distribution from that of the train, in which case it is named as external validation set [45]. For example, data from two consecutive harvest seasons are used as train set, while the test set uses data from a third season. A similar situation can be envisaged by using a train set collected from different orchards or origins, than that used for validation. Yet, given the large amount of time invested in acquiring this type of data, multi-seasonal or multi-orchards test data sets are not often found in the literature. In contrast, laboratory models with homogeneous fruit sets are abundant (Tables 2–4). Currently, the most common procedure when constructing and validating Vis-NIRS models for the various fruit QA is to separate a fraction of the available samples as train/calibration set (usually 80%), and the remaining as test/validation set (usually 20%). Furthermore, the validation samples are typically chosen as the best possible representation of the whole set and within the variation range of the train set. This has been applied even when the models comprehend several species and/or cultivars, orchard locations and harvest years, which are mixed in the calibration and validation sets [68]. All studies included in Tables 2–4 that used this approach, were labeled as internal in the validation column. Internal validation does not ensure the success of a continuous monitoring application, which is a dynamic and open process, particularly, if one aims to use the Vis-NIRS in real world applications, being in inline grading systems or handheld devices. Once the model is applied to the test set and a final prediction is made, one can assess how well the model performs by computing several metrics. If the model was developed for regression, the most used are the root mean squared error (RMSE), bias, the slope, the coefficient of determination (R2), and the standard deviation ratio (SDR) or ratio of performance to deviation (RPD) or residual standard deviation (RSD). If the model is developed for classification, the advised metrics are accuracy (ACC), F1 score and receiver operating characteristic (ROC) curve. For completeness, these metrics are often computed not only for the test set, but also for the calibration, and tuning sets as well. The comparison between calibration (C), tuning (CV) and test set (P) error metrics allows to understand how well the model generalizes, i.e. how the information learned by the model during training transposes to the final external validation dataset.
\n
\n
\n
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4. Prediction of quality attributes
\n
Vis-NIRS combined with various chemometric methods has produced calibration models to predict simultaneously multiple QA of various citrus species and varieties, which are presented in Tables 2–4. These attributes range from fruit size, weight and color [68] to SSC, MI, external and internal defects, several compounds such as sugars, acids, pigments and antioxidants. As expected, these models address predominantly several varieties of orange and mandarin, but also grapefruit, lime, pomelo, sweet lemon and tangelo. The spectral ranges used cover the whole Vis-NIRS range. The majority of the Vis-NIRS calibration models were obtained from samples collected and assessed under controlled conditions in the laboratory, after fruit temperature equilibration, either with benchtop or handheld devices (Tables 2 and 3). Despite the large market availability of the latter, presenting different levels of portability, spectral ranges, sizes, and prices, only a few studies have focused on its application to assess the quality and ripening of oranges and mandarins on-tree (Table 4), perhaps due to the complexities involved under field conditions, and the performance deterioration of calibration models, in spite of the spectral range used [63, 64, 65, 66, 67, 68]. Nevertheless, the QA assessed on-tree (Table 4) comprise fruit mass and size, color parameters [63, 67, 68], pericarp thickness, SSC, TA, firmness, MI, juice pH and mass, and BrimA index, which measures the balance between sweetness and acidity as described by [12]. Noteworthy, the majority of the calibration models exhibited \n\n\nR\n2\n\n<\n0.8\n\n, despite the range used, and did not include external validation, except for [65, 66].
\n
Grading lines equipped with Vis-NIR sensors are now commercially available from various companies, to assess both the EQA and IQA of citrus fruit [38, 39, 40]. Unfortunately, the scientific evidence about the accuracy of these systems is very scarce, due to the ‘industrial secrecy’. Nevertheless, there are a few cases of partnership among the industrial sector and the research groups to know the real applicability and their performance in assessing citrus fruit QA by such equipment [91], in most cases still in the prototype stage, as reported by [92, 93]. [91] evaluated the performance of a customized NIR equipment installed underneath the fruit conveyor to sort oranges and mandarins in a Spanish packinghouse. This system working in a transmittance mode in the 650–970 nm spectral range, only provided calibration models that could discriminate between low and high values of SSC for both mandarin (R = 0.76–0.86; SEP \n\n∼\n\n 0.9 °Brix; RPD \n\n∼\n\n 0.74) and orange (R = 0.87; SEP \n\n∼\n\n 0.7 °Brix; RPD \n\n<\n\n 1.5). No acceptable models were obtained for TA in neither species. [92] evaluated the performance of a Vis-NIRS system to assess the SSC of ‘Indian River’ red grapefruit and ‘Honey’ tangerine from Florida in a sorting inline prototype, with R2 ranging from 0.15 to 0.67. The prototype percent correct classification averaged 85% for SSC at 10 °Brix and 79% for an 11 °Brix setpoint in the second-year of tests. Otherwise, [93] reported on the development and laboratory testing of the nondestructive citrus fruit quality monitoring prototype system, which consisted of a light detection and ranging (LIDAR) and Vis-NIRS sensors installed on an inclined conveyor for mimicking real-time fruit size and SSC measurement respectively, during harvest. Laboratory tests in ‘Valencia’ orange revealed that the system was applicable for instantaneous fruit size (R2 = 0.91) and SSC (R2 = 0.677, SEP = 0.48 °Brix) determination.
\n
The various Vis-NIRS calibration models presented in this chapter, show different levels of accuracy, prediction and robustness for the various attributes, SSC being the most successfully IQA assessed at all spectral ranges, independently of the devices used (Tables 2–4). Both juice pH and vitamin C also seem easily assessed by devices operating in the Vis-SWNIRS range devices [55, 94, 95], but TA has been shown to require wavelengths range \n\n>\n\n 1000 nm [13, 96]. Additionally, calibration models for firmness have been difficult to obtain, although there are a few exceptions reported for several orange varieties, in the reflectance mode and in the ranges 500–1690 nm [67], and 1000–2500 nm [73]. Of course, the calibration models for specific compounds, such as sugars, acids or antioxidants, require in most cases the longer NIR spectral range [12, 69, 74].
\n
Both external and internal defects in citrus have been successfully predicted by Vis-NIRS, such is the case of the section drying in tangerine (transmittance; 780 and 960 nm) [10], oleocellosis in ‘Trovita’ orange (reflectance; 400–1000 nm) [71], the freeze damage in sweet lemon (half-transmittance; 400–110 nm) [13], the rind pitting in ‘Marsh’ grapefruit (reflectance; 400–2500) [12] or the granulation in ‘Shantian’ pomelo (transmittance; 400–700) [75]. However, [97] was not able to predict the external disorder RBD in ‘Nules Clementine’ mandarin (interactance; range 450–1000 nm).
\n
Among the chemometrics methods used to construct the calibration models, PLS is still the main linear regression technique used, and the one to produce the best models for the widest number of QA (Tables 2–4). However, there are some exceptions, regarding the use of non-linear techniques, which were shown to deliver calibration models with equivalent or even better prediction and accuracy for several QA than PLS. Among these, there is the WT-LSSVR, BP-NN and LS-SMV that provided models with higher prediction capacity for SS in ‘Gannan’ orange [58], SSC, TA and vitamin C in Nanfeng mandarin [55] and in ‘Newhall’ orange [96], respectively. The LOCAL algorithm has also shown to produce better models than MPLS for firmness and juice mass in ‘Powell Summer Navel’ orange [67], and in ‘Clemenvilla’ mandarin [68]. SIMCA, SVM and particularly PCA-ANN, also allowed to assess with a total accuracy \n\n>\n\n98% the freeze damage in sweet lemon [13] and PCA-GRNN allowed the assessment of the granulation in ‘Honey’ pomelo at a classification accuracy (CA) \n\n>\n\n95% [14].
\n
Independently of the chemometrics technique used, for the majority of the models presented in Tables 2–4, the train and test fruit sets were chosen from the same batch, orchards or seasons. Even when the whole data set comprised fruit from several orchards, harvest season or citrus varieties, the usual approach was to randomly choose 80% of the whole data set to construct the calibration model and 20% to validate it, as reported by [68, 97]. A truly stringent external validation is thus required to have a realistic idea of the models’ performance in orchard and/or cargo batch monitoring. External validation means validation through a dataset with a different origin (spatial or temporal) relatively to the datasets used in calibration. Nevertheless, there are some clear examples of this approach, such as previously reported for mandarin [53, 56, 97, 98, 99], orange [12, 56, 65, 66, 70, 72, 73, 97], and grapefruit [12, 70]. Without the effective external validation, it is not possible to know exactly how well these models would work in real conditions due to the large variability within the trees, orchards, sites and harvest seasons. Yet, a certain degree of deterioration of the initial model prediction is expected, which would warrant further attention. This has been reported by [54, 65, 66]. Yet, there is space and potential for improvement beyond the ‘proof of concept’, if one aims to use these devices on the daily routines of the orchards’ management. This has been suggested by [55, 66] through model recalibration using a few fruits from the new harvest season/orchard, or by achieving a strong degree of robustness by constructing a multi-seasonal and multi-orchard model as reported by [67] for ‘Newhall’ orange, which will be much more advantageous when assessing the ripening of fruit on-tree.
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5. Future research and perspectives
\n
Vis-NIRS has been incorporated by a large number of companies in commercial applications to be used on inline, benchtop and handheld systems. However, there are several topics regarding the full potential and limitations of this technology that require attention and further research in order to provide the consistency warranted by the daily basis routines of the citrus supply chain when assessing fruit quality and ripening. Firstly, all researchers engaged in this area should report their results in a uniform way, particularly in what respects the obtained models’ metrics [38]. This would allow a better understanding on the effective advances and contributions made by each study. Other models’ metrics parameters, such as the prediction gain, may also be useful, as reported by [100]. Secondly, the calibration models’ robustness must be addressed and solved through a stringent multi-year, multi-cultivar and multi-orchard validation, such as previously reported by [66]. The usual approach of validating calibration models with a random fraction of the total available data set, even when the models comprehend several varieties, orchard sites and harvest years does not ensure the success of a continuous monitoring application and delivers unrealistic performance metrics [53, 54]. The usual recalibration and spiking approaches used to improve the initial calibration models with a few fruits from independent data sets that will be then assessed, assume that those fruits used to recalibrate/update the model constitute a faithful representation of the new population and are common techniques in various commercial devices, for inline and benchtop systems. However, this becomes quite difficult to apply if one aims to monitor the on-tree fruit ripening evolution through time, for the fruit sampled in the first weeks cannot represent those to be measured in the last weeks of the harvest season. Thirdly, there is a large potential for models’ improvement, by using the non-linear techniques of machine learning, and those of deep learning. Fourthly, there is much to understand on the effect of the rind in the assessment of the pulp IQA in citrus fruit, since the NIR radiation hardly gets to the fruit pulp, and both biochemical and optical properties have a major role to play in the spectral data acquired [47, 49, 95, 101, 102]. Fifthly, the calibration models should be able to predict attributes that are closer to the organoleptic evaluation of the fruit. It is the case of BrimA index, a better indicator of fruit sweetness that the SSC, which was satisfactorily predicted in orange, grapefruit and mandarin [12, 64]. Finally, the handheld devices must really be tested under field conditions, if one aims to assess the fruit on-tree, which is essential for the OHD decision.
\n
\n
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6. Conclusions
\n
The usefulness of Vis-NIRS combined with different chemometric techniques in the supply chain of citrus fruit is already quite extensive and growing, similarly to many other commodities. In this chapter the authors only addressed the classic spectral ‘point’ measurements, but it is quite clear that both inline, benchtop and handheld devices are used to assess nondestructively multiple QA in various citrus species and cultivars, with a clear predominance of orange and mandarin. Among these attributes, there are both EQA and IQA, as well as defects caused by various factors, such as physiological disorders. The devices available on the market are from various brands, operate in various ranges and present a wide variety of prices. Aside from the “proof of concept” made by many studies, that the authors tried to comprise as much as possible in this chapter, there are several issues that still need to be addressed by researchers, a major one being the need for a stringent external validation of the calibration models, in order to assure robustness and to fulfill with the essential requirements to include this technology in the daily routines of these crop supply chain. This is of the utmost importance when considering the assessment of fruit ripening on-tree to determine the optimal harvest date for each orchard, or sections of the orchard. This is highly significant based on the determinant effect of producing and harvesting the fruit at its best ripening stage, thus assuring the best quality throughout the whole postharvest and shelf-life. As a concluding remark, it is very important to add that these devices are of medium and high cost, and that are not the kind of technology to ‘set and forget’, as reiterated by [39], which demands not only for a budget to acquire the systems, but also to maintain them, and to keep the continuous update and improvement of the calibration models, that in most cases need the selling company assistance. Thus, there must be a cost–benefit that both the producers and packinghouses have to meet through the added commercial value to citrus fruit by these systems, and the consumer willingness to pay for fresh fruit graded in terms of IQA such as sugar content, acidity and nutraceutical properties.
\n
\n
Acknowledgments
\n
The authors acknowledge FCT - Fundação para a Ciência e a Tecnologia, Portugal, for funding CEOT project UIDB/00631/2020 CEOT BASE and UIDP/ 00631/2020 CEOT PROGRAMATICO, and MED- Project UIDB/05183/2020. Dário Passos was funded by project OtiCalFrut (ALG-01-0247-FEDER-033652). Rosa Pires was funded through a BI fellowship from project NIBAP (ALG-01-0247-FEDER-037303).
\n
Conflict of interest
There are no conflicts of interest.
\n',keywords:"nondestructive, Vis–NIRS, citrus fruit, quality, ripening",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/75090.pdf",chapterXML:"https://mts.intechopen.com/source/xml/75090.xml",downloadPdfUrl:"/chapter/pdf-download/75090",previewPdfUrl:"/chapter/pdf-preview/75090",totalDownloads:30,totalViews:0,totalCrossrefCites:0,dateSubmitted:"September 24th 2020",dateReviewed:"January 12th 2021",datePrePublished:"February 4th 2021",datePublished:null,dateFinished:"February 4th 2021",readingETA:"0",abstract:"As non-climacteric, citrus fruit are only harvested at their optimal edible ripening stage. The usual approach followed by producers and packinghouses to establish the internal quality and ripening of citrus fruit is to collect fruit sets throughout ripening and use them to determine the quality attributes (QA) by standard and, in many cases, destructive and time-consuming methods. However, due to the large variability within and between orchards, the number of measured fruits is seldom statistically representative of the batch, resulting in a fallible assessment of their internal QA (IQA) and a weak traceability in the citrus supply chain. Visible/near-infrared reflectance spectroscopy (Vis–NIRS) is a nondestructive method that addresses this problem, and has proved to predict many IQA of a wide number of fruit including citrus. Yet, its application on a daily basis is not straightforward, and there are still several questions to address by researchers in order to implement it routinely in the crop supply chain. This chapter reviews the application of Vis–NIRS in the assessment of the quality and ripening of citrus fruit, and makes a critical evaluation on the technique’s limiting issues that need further attention by researchers.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/75090",risUrl:"/chapter/ris/75090",signatures:"Ana M. Cavaco, Dário Passos, Rosa M. Pires, Maria D. Antunes and Rui Guerra",book:{id:"8108",title:"Citrus",subtitle:null,fullTitle:"Citrus",slug:null,publishedDate:null,bookSignature:"Prof. Muhammad Sarwar Khan",coverURL:"https://cdn.intechopen.com/books/images_new/8108.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"212511",title:"Prof.",name:"Muhammad Sarwar",middleName:null,surname:"Khan",slug:"muhammad-sarwar-khan",fullName:"Muhammad Sarwar Khan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Fundamentals of visible–near infrared reflectance spectroscopy (Vis–NIRS)",level:"1"},{id:"sec_2_2",title:"2.1 Interaction of radiation with the fruit",level:"2"},{id:"sec_3_2",title:"2.2 ‘Point’ and imaging measurements",level:"2"},{id:"sec_4_2",title:"2.3 Instrumentation and measurement setup",level:"2"},{id:"sec_5_2",title:"2.4 Typical Vis-NIRS spectrum and its interpretation",level:"2"},{id:"sec_7",title:"3. Chemometrics",level:"1"},{id:"sec_7_2",title:"3.1 Spectral pre-processing and outlier detection",level:"2"},{id:"sec_8_2",title:"3.2 Clustering",level:"2"},{id:"sec_9_2",title:"3.3 Classification and regression models",level:"2"},{id:"sec_10_2",title:"3.4 The quality of a calibration model",level:"2"},{id:"sec_12",title:"4. Prediction of quality attributes",level:"1"},{id:"sec_13",title:"5. Future research and perspectives",level:"1"},{id:"sec_14",title:"6. Conclusions",level:"1"},{id:"sec_15",title:"Acknowledgments",level:"1"},{id:"sec_18",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nFAO - Citrus fruit fresh and processed statistical bulletin, 66 pp. 2017. Available from: http://www.fao.org/publications [Accessed: 2020-10-20]\n'},{id:"B2",body:'\nLiu Y, Heying E, Tanumihardjo SA. History, Global Distribution, and Nutritional Importance of Citrus Fruits. Comprehensive Reviews in Food Science and Food Safety. 2012; 11(6): 530–545. doi.org/10.1111/j.1541-4337.2012.00201.x\n'},{id:"B3",body:'\nCodex Alimentarius - International Food Standards [Internet]. 2020 Available from: http://www.fao.org/fao-who-codexalimentarius/codex-texts/list-standards/En/ [Accessed: 2020-12-07]\n'},{id:"B4",body:'\nLado J, Rodrigo MJ, Zacarías, L. Maturity indicators and citrus fruit quality. Stewart Postharvest Review. 2014; 2 (2): 1–6.\n'},{id:"B5",body:'\nLadaniya MS. 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CEOT—Center for Electronics, Optoelectronics and Telecommunications, University of Algarve, Portugal
CEOT—Center for Electronics, Optoelectronics and Telecommunications, University of Algarve, Portugal
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Our business values are based on those any scientist applies to their research. The values of our business are based on the same ones that all good scientists apply to their research. We have created a culture of respect and collaboration within a relaxed, friendly, and progressive atmosphere, while maintaining academic rigour.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
\n\n
If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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