Nutrient composition of cereal grains.
\r\n\t
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Cereal | Protein (%) | Fat (%) | Crude fiber (%) | Ash (%) | Starch (%) | Total dietary fiber (%) | Total phenol (mg/100 g) |
---|---|---|---|---|---|---|---|
Rice | 7.5 | 2.4 | 10.2 | 4.7 | 77.2 | 3.7 | 2.51 |
Wheat | 14.4 | 2.3 | 2.9 | 1.9 | 64.0 | 12.1 | 20.5 |
Maize | 12.1 | 4.6 | 2.3 | 1.8 | 62.3 | 12.8 | 2.91 |
Barley | 11.5 | 2.2 | 5.6 | 2.9 | 58.5 | 15.4 | 16.4 |
Sorghum | 11 | 3.2 | 2.7 | 1.8 | 73.8 | 11.8 | 43.1 |
Oats | 17.1 | 6.4 | 11.3 | 3.2 | 52.8 | 12.5 | 1.2 |
Rye | 13.4 | 1.8 | 2.1 | 2.0 | 68.3 | 16.1 | 13.2 |
Finger millet | 7.3 | 1.3 | 3.6 | 3.0 | 59.0 | 19.1 | 10.2 |
Pearl millet | 14.5 | 5.1 | 2.0 | 2.0 | 60.5 | 7.0 | 51.4 |
Foxtail millet | 11.7 | 3.9 | 7.0 | 3.0 | 59.1 | 19.1 | 106 |
Bangladesh, a low-lying, riverine country, lies in the north-eastern part of South Asia between latitude 20034′ and 26038’ N and longitude 88001′ and 92041′ E. The country, with an area of 147,570 sq. km (56,977 sq. mi), is bounded by India on the west-north and north-east while Myanmar on the south-east and the Bay of Bengal on the south [3]. Bangladesh, predominantly an agrarian country, enjoys generally a subtropical monsoon climate. The country comprises a wide range of agro-ecosystems spread over the wetlands, (deltaic) flood plains as well as the hills. The agriculture sector contributes about 14.23% of the country’s GDP and employs around 40.60% of the total labour force [4]. Due to its very fertile land and favorable weather conditions, a wide diversities of crops e.g., cereals, pulses, oilseeds, spices and condiments, fibers, vegetables, etc. grow abundantly in this country. Cereal crops occupied more than 75% of the total cropped area of Bangladesh [4].
Among the cereal crops, rice is the staple food for millions across the globe including Bangladesh. In Bangladesh, rice occupies more than 96% of the land area under “Cereal Agriculture”. Bangladesh is the third-largest rice producer in the world after China and India [5]. Maize occupies the 2nd position both in acreage and production, but its production is insufficient to meet the national demand, followed by wheat and other minor cereals
No. | Cropping pattern | Area (ha) | % of NCA | District (no.) | Upazila (no.) |
---|---|---|---|---|---|
001 | Boro−Fallow−T. Aman | 2306005 | 26.919 | 63 | 426 |
002 | Boro−Fallow−Fallow | 1139530 | 13.302 | 59 | 342 |
003 | Fallow−Fallow−T. Aman | 509480 | 5.947 | 36 | 162 |
004 | Boro−Aus − T. Aman | 209015 | 2.440 | 47 | 177 |
005 | Fallow−Aus − T. Aman | 193275 | 2.256 | 30 | 108 |
006 | Mustard−Boro−T. Aman | 184620 | 2.155 | 51 | 203 |
007 | Boro−B. Aman | 183070 | 2.137 | 32 | 113 |
008 | Potato−Boro−T. Aman | 180380 | 2.106 | 33 | 115 |
009 | Wheat−Jute−T. Aman | 147210 | 1.718 | 43 | 216 |
010 | Vegetable−Vegetable−Vegetable | 143270 | 1.672 | 61 | 283 |
011 | Mustard−Boro−Fallow | 143130 | 1.671 | 37 | 112 |
012 | Grasspea−Fallow−T. Aman | 108150 | 1.262 | 25 | 80 |
013 | Maize−Fallow−T. Aman | 101460 | 1.184 | 39 | 126 |
014 | Wheat−Fallow−T. Aman | 90910 | 1.061 | 39 | 100 |
015 | Mungbean−Fallow−T. Aman | 89650 | 1.047 | 22 | 70 |
016 | Grasspea−Aus − T. Aman | 81610 | 0.953 | 19 | 61 |
017 | Vegetable−Fallow−T. Aman | 74710 | 0.872 | 45 | 170 |
018 | Vegetable−Vegetable−Fallow | 63935 | 0.746 | 59 | 168 |
019 | Onion−Jute−T. Aman | 54185 | 0.633 | 39 | 102 |
020 | Mungbean−Aus − T. Aman | 53730 | 0.627 | 14 | 43 |
021 | Chili−Fallow−T. Aman | 52995 | 0.619 | 45 | 146 |
022 | Lentil−Jute−T. Aman | 51875 | 0.606 | 34 | 96 |
023 | Vegetable−Vegetable−T. Aman | 51745 | 0.604 | 49 | 127 |
024 | Wheat−Jute−Fallow | 48700 | 0.568 | 32 | 82 |
025 | Potato−Maize−T. Aman | 47690 | 0.557 | 19 | 68 |
In this chapter, the historical development and production scenario of different cereal crops of Bangladesh, their present status, constraints, challenges and opportunities have been described and discussed.
In our tradition, rice is synonymous with food, the world’s second-largest per capita rice consumption at 179.9 kg yr.−1 [8], and is dominating the entire crop sector. It provides nearly 48% of rural employment, about two-thirds of the total calorie supply and one-half of the total protein intake of an average person in the country. The rice sector contributes one-half of the agricultural GDP and one-sixth of the national income in Bangladesh [3]. Due to favorable weather conditions (e.g., temperature, relative humidity, rainfall, day length, etc.), rice is grown all the year-round in three growing periods
The total rice coverage was about 11.52 million hectares (m ha) over three rice growing seasons in 2018–2019 (Table 3). Most of the modern rice cultivars are photoperiod insensitive, therefore, they could be cultivated almost throughout the year. Even in some specific ecosystems, farmers may harvest three rice crops a year from the same piece of land. The recent coverage of Aus, Aman and Boro area were 9.60, 48.82 and 41.58 per cent, respectively. Boro and Aman contributed 53.75 and 38.62 per cent, respectively of the total rice production whereas Aus only 7.63 per cent, although total production of Aus rice increasing very slowly [9].
Though the total rice-growing area did not change much during the last four and a half decades (Figure 1), rice production nearly quadrupled from 9.8 million metric tons (m t) in 1971–1972 to 36.4 m t in 2019, helping Bangladesh to achieve self-sufficiency in rice production and ensuring food security. There had been a major shift in ecotype based (Boro-Aus-Aman) rice cultivation. The area under HYVs of Boro rice was 0.32 m ha in 1971–1972, 4.11 m ha in 2007–2008 and 4.79 m ha in 2018–2019. Most of the traditional Aus cultivars were in the process of replacement with the introduction of HYVs. Around 50 per cent of the traditional Deep Water Rice (DWR) lands were transformed into irrigated Boro land [9]. In 1971–1972 traditional Aus coverage was 2.95 m ha. More than two-thirds of the Aus area was given up mostly to Boro by 2014–2015. The coverage under Aman has experienced little change since 1971. The trends in area coverage and production under different rice ecotypes are described and discussed in detail in [9]. Recently, researchers of the Bangladesh Rice Research Institute (BRRI) and their collaborators had developed the rice vision leading to 2050 and beyond for Bangladesh [11]. They reported that rice production could reach 47.2 m t, having a surplus of 2.6 m t in 2050 and targeted to be continued thereafter, at the present increment rate of rice production. Several measures were also recommended to achieve the rice vision of Bangladesh leading to 2050 and beyond [11]. Although rice is the component of most of the cropping patterns of Bangladesh, 17 cropping patterns exclusively contained rice crops [7]. Five of them were most dominant among cropping patterns of Bangladesh.
Area coverage and production trend of rice. M ha million hectare; m t million metric ton. Source: BBS [
Bangladesh was very rich in rice genetic resources. Name of nearly 12,500 traditional cultivars, those were cultivated in different seasons of Bangladesh, were listed [12]. The International Rice Research Institute (IRRI) Gene Bank contains more than 8,000 traditional rice cultivars collected from Bangladesh. Rice breeders used many of these landraces as donors to develop elite lines that have been used as parents for popular improved rice cultivars grown throughout Asia [13]. The Genetic Resource and Seed Division of Bangladesh Rice Research Institute (BRRI) has collected and conserved more than 8,000 landraces of rice were as long medium, and short-term storage (Table 4). Most of the traditional cultivars are out of cultivation due to comparatively low yield, although these have many exceptional qualities e.g., fineness, taste, aroma, etc. Only around eight per cent of the recorded landrace cultivars are still available with the farmers in some fragile pocket areas like saline, drought, deep water area and hilly areas of Bangladesh [13]. In recent years, the cultivation of traditional rice cultivars with exceptional features e.g., long grains, fineness, taste, aroma, etc. is retrieving popularity for a premium price, customer’s preferences, national and international demand, etc. Presently, one specialized research institute, the BRRI and a few other organizations like Bangladesh Institute of Nuclear Agriculture (BINA), Bangladesh Agricultural University (BAU), are working on the development of high yielding rice cultivars, both inbred and hybrids, for different seasons (Table 5). Seeds of some hybrid cultivars are imported by different organizations and seed companies from different countries.
Season | Coverage (m ha) | Total production (m t) | Yield (t ha−1) | % of total area | % of total production |
---|---|---|---|---|---|
1.11 | 2.78 | 2.51 | 9.60 | 7.63 | |
5.62 | 14.06 | 2.50 | 48.82 | 38.62 | |
4.79 | 19.56 | 4.08 | 41.58 | 53.75 | |
Total | 11.52 | 36.30 | — | — |
Cultivar/Line | Registered in accession |
---|---|
Indigenous | |
Local landraces | 5202 |
Pure line selection | 1030 |
Exotic | 790 |
Exotic/breeding lines | 968 |
Wild Rice of Bangladesh ( | 42 |
Wild rice from IRRI | 12 |
Total | 8044 |
Season | Cultivar |
---|---|
Aus | Broadcast – BR20, BR21, BR24, BRRI dhan27, BRRI dhan42, BRRI dhan43, BRRI dhan65 and BRRI dhan83. |
Transplant – BR1, BR2, BR3, BR6, BR7, BR8, BR9, BR14, BR16, BR26, BRRI dhan27, BRRI dhan48, BRRI dhan55, BRRI dhan82, BRRI dhan85, BRRI dhan98 and BRRI hybrid dhan7; Iratom 24, Binadhan-19. | |
Aman | BR3, BR4, BR5, BR10, BR11, BR22, BR23, BR25, BRRI dhan30, BRRI dhan31, BRRI dhan32, BRRI dhan33, BRRI dhan34, BRRI dhan37, BRRI dhan38, BRRI dhan39, BRRI dhan40, BRRI dhan41, BRRI dhan44, BRRI dhan46, BRRI dhan49, BRRI dhan51, BRRI dhan52, BRRI dhan53, BRRI dhan54, BRRI dhan56, BRRI dhan57, BRRI dhan62, BRRI dhan66, BRRI dhan70, BRRI dhan71, BRRI dhan72, BRRI dhan73, BRRI dhan75, BRRI dhan79, BRRI dhan80, BRRI dhan87, BRRI dhan90, BRRI dhan91, BRRI dhan93, BRRI dhan94, BRRI dhan95, BRRI hybrid dhan4 and BRRI hybrid dhan6; Binashail, Binadhan-4, Binadhan-7, Binadhan-11, Binadhan-12, Binadhan-13, Binadhan-15, Binadhan-16, Binadhan-17, Binadhan-19, Binadhan-21, Binadhan-22, Binadhan-23; BAU dhan1, BAU dhan2. |
Boro | BR1, BR2, BR3, BR6, BR7, BR8, BR9, BR12, BR14, BR15, BR16, BR17, BR18, BR19, BR26, BRRI dhan28, BRRI dhan29, BRRI dhan35, BRRI dhan36, BRRI dhan45, BRRI dhan47, BRRI dhan50, BRRI dhan55, BRRI dhan58, BRRI dhan59, BRRI dhan60, BRRI dhan61, BRRI dhan63, BRRI dhan64, BRRI dhan67, BRRI dhan68, BRRI dhan69, BRRI dhan74, BRRI dhan81, BRRI dhan84, BRRI dhan86, BRRI dhan88, BRRI dhan89, BRRI dhan92, BRRI dhan96, BRRI dhan97, BRRI dhan99, BRRI dhan100, BRRI hybrid dhan1, BRRI hybrid dhan2, BRRI hybrid dhan3 and BRRI hybrid dhan5; Binadhan-5, Binadhan-6, Binadhan-8, Binadhan-10, Binadhan-14, Binadhan-18, Binadhan-24; BAU dhan3. |
Seasonal distribution of modern, both inbred and hybrid, rice cultivars in Bangladesh.
Source: BRRI [15]; http://www.bina.gov.bd/;http://www.sca.gov.bd/
Wheat, one of the first cultivated plants, possesses unique dough-forming properties and is the leading source of plant (cereal) protein in the human diet, having higher protein content (14.4%) compared to other major cereals i.e., maize (corn) and rice (12.1 and 7.5%, respectively) (Table 1). In terms of total production tonnages used for food, it is currently second to rice as the main human food crop and ahead of maize, allowing for more extensive use in animal feeds. The increasing income level and urbanization lead to dietary changes such as switching from traditional rice to wheat and to livestock, poultry, and fish products, which in turn require large amounts of maize for their production [16].
In Bangladesh, it is a crop of Rabi (Winter; Mid-October to Mid-March) season; it requires dry weather, bright sunlight and well-distributed rainfall between 40 and 110 cm for congenial growth. Although wheat has some advantages in its cultivation compared to Boro and other winter crops i.e., less water requirement, echo-friendly, high nutritional value, diversified use, etc.; the command area under wheat cultivation showed a decreasing trend (Figure 2). In 1971–1972, the coverage was 0.127 m ha and the total production was only 0.113 m t. Since then the coverage area remarkably went up to 0.88 m ha in 1998–1999 which is almost 7 times in 27 years. However, the area declined to 0.39 m ha in 2006–2007 and maintained more or less the same level up to 2011–2012, thereafter, an increasing trend up to 2015–2016 and the 0.33 m ha in 2018–2019 (Figure 2). The total production followed the same trend until 2006–2007 having the highest peak (1.90 m t) in 1998–1999. However, despite a small increase in the coverage area (compared to 1971–1972), the production trend is quite inspiring (Figure 2). This might be due to the application of innovative approaches in wheat research and development [9]. A specialized research institute, the Bangladesh Wheat and Maize Research Institute (BWMRI) has very recently been established in 2017. Formerly, it was a (Wheat) Research Centre under the Bangladesh Agricultural Research Institute (BARI). Until today 33 high yielding wheat cultivars are developed by BARI (Table 6). Just getting separated from BARI very recently, BWMRI has released three cultivars,
Area coverage and production trend of wheat. M ha million hectare; m t million metric ton. Source: BBS [
Serial Number | Name of cultivar | Year of Release | Yield (t ha−1) |
---|---|---|---|
1 | Kalyansona | 1968 | 2.6–3.2 |
2 | Sonora 64 | 1974 | 1.6–2.2 |
3 | Norteno 67 | 1974 | 2.8–3.2 |
4 | Mexi 65 | 1974 | 2.6–3.6 |
5 | Inia 66 | 1974 | 2.5–3.0 |
6 | Sonalika | 1974 | 3.0–3.5 |
7 | Tanori 71 | 1975 | 2.8–3.2 |
8 | Jupateco 73 | 1975 | 3.0–3.2 |
9 | Nuri 70 | 1975 | 2.5–3.0 |
10 | Balaka | 1979 | 2.6–3.0 |
11 | Doel | 1979 | 2.5–3.0 |
12 | Pavon 76 | 1979 | 3.0–3.6 |
13 | Akbar | 1983 | 3.5–4.5 |
14 | Kanchan | 1983 | 3.5–4.5 |
15 | Ananda (BAW 18) | 1983 | 2.1–3.4 |
16 | Barkat | 1983 | 3.4–3.8 |
17 | Agrahani | 1987 | 3.5–4.0 |
18 | Protiva | 1993 | 3.5–4.5 |
19 | BARI Gom −19 (Sourav) | 1998 | 3.5–4.5 |
20 | BARI Gom −20 (Gourab) | 1998 | 3.6–4.8 |
21 | BARI Gom −21 (Shatabdi) | 2000 | 3.6–5.0 |
22 | BARI Gom −22 (Sufi) | 2005 | 3.6–5.0 |
23 | BARI Gom −23 (Bijoy) | 2005 | 4.3–5.0 |
24 | BARI Gom −24 (Prodip) | 2005 | 4.3–5.1 |
25 | BARI Gom-25 | 2010 | 3.6–5.0 |
26 | BARI Gom-26 | 2010 | 3.6–5.0 |
27 | BARI Gom −27 | 2012 | 4.0–5.4 |
28 | BARI Gom −28 | 2012 | 4.0–5.5 |
29 | BARI Gom −29 | 2014 | 4.0–5.0 |
30 | BARI Gom −30 | 2014 | 4.5–5.5 |
31 | BARI Gom −31 | 2017 | 4.5–5.0 |
32 | BARI Gom −32 | 2017 | 4.6–5.0 |
33 | BARI Gom −33 | 2017 | 4.0–5.0 |
Modern wheat cultivars developed by Bangladesh Agricultural Research Institute.
Source: Azad
Maize, indigenous to the Americas and staple in South and Central America and Southern Africa, occupied the second position both in area and production and mainly used for animal and poultry feed industries in Bangladesh. It was an insignificant crop, still reported as a minor cereal in Bangladesh perspective [4], and a little development was observed until 2000. Then the area started increasing progressively while the total production increased quite significantly (Figure 3). Maize is now cultivated in both Rabi (Winter; Mid-October to Mid-March) and Kharif-1 (Early monsoon; Mid-March to Mid-July) seasons, and area and production of maize increased considerably. Now, it secured second position pushing wheat to third. In 1971–1972, the coverage and total production were 0.0028 m ha and 0.002 m t respectively which increased to 0.445 m ha to produce 3.569 m t in 2019 (Figure 3). The corresponding increments in percentages were
Area coverage and production trend of maize. Source: BBS [
Serial Number | Name of cultivar | Season | Yield (t ha−1) |
---|---|---|---|
1 | Shuvra | Rabi | 4.5–5.5 |
2 | Khoibhutta | Rabi, Kharif | Rabi-3.5-4.0, Kharif-2.5-3.5 |
3 | Barnali | Rabi, Kharif | Rabi-5.5-6.0, kharif-4.0-4.5 |
4 | Mohor | Rabi, Kharif | Rabi-5.0-5.5, Kharif-3.5-4.5 |
5 | BARI Maize-5 | Rabi, Kharif | Rabi-6.5-7.5, Kharif-5.0-6.0 |
6 | BARI Maize-6 | Rabi, Kharif | Rabi-6.5-7.5, Kharif-5.0-6.0 |
7 | BARI Maize-7 | Rabi, Kharif | Rabi-6.5-7.5, Kharif-5.0-6.0 |
8 | BARI Sweet Corn-1 | Rabi | 10.5 |
9 | BARI Baby Corn-1 | Rabi | 1.27–1.30 |
10 | BARI Hybrid Maize-1 | Rabi, Kharif | Rabi-7.5-8.5, Kharif-6.5-7.0 |
11 | BARI Hybrid Maize-2 | Rabi, Kharif | Rabi-9.0-9.5, Kharif-7.0-7.5 |
12 | BARI Hybrid Maize-3 | Rabi, Kharif | Rabi- 10-10.5, Kharif-7.0-7.5 |
13 | BARI Hybrid Maize-4 | Rabi, Kharif | Rabi- 9.0-9.5, Kharif-7-7.5 |
14 | BARI Hybrid Maize-5 | Rabi, Kharif | Rabi- 9-10, Kharif-7.0-7.5 |
15 | BARI Hybrid Maize-6 | Rabi, Kharif | Rabi- 9.0-9.5, Kharif-7-7.5 |
16 | BARI Hybrid Maize-7 | Rabi, Kharif | Rabi- 10.0-11.0, Kharif-7-7.5 |
17 | BARI Hybrid Maize-8 | Rabi, Kharif | Rabi- 10.0-11.5, Kharif-7-7.5 |
18 | BARI Hybrid Maize-9 | Rabi, Kharif | Rabi- 11.5-12.5 |
19 | BARI Hybrid Maize-10 | Rabi, Kharif | Rabi- 10.0-11.5 |
20 | BARI Hybrid Maize-11 | Rabi, Kharif | Rabi- 10.5-11.5 |
21 | BARI Hybrid Maize-12 | Rabi | 10.0–11.1 |
22 | BARI Hybrid Maize-13 | Rabi | 8.1–8.9 |
23 | BARI Hybrid Maize-14 | Rabi, Kharif | Rabi- 10.84, Kharif-10.52 |
24 | BARI Hybrid Maize-15 | Rabi, Kharif | Rabi- 12.75, Kharif-12.07 |
25 | BARI Hybrid Maize-16 | Rabi | 11.57 |
26 | BARI Hybrid Maize-17 | — | — |
Modern maize cultivars developed by Bangladesh Agricultural Research Institute.
Source: Azad
Barley, one of the oldest cereal crops, ranked fourth among grains behind maize, rice, and wheat. It is widely grown in marginally productive soils across the world points to the high adaptability of the genus
Area coverage and production trend of barley. T ha thousand hectare; t t thousand metric ton. Source: FAOSTAT 2020
Serial Number | Name of cultivar | Year of Release | Yield (t ha−1) |
---|---|---|---|
1. | BARI Barley-1 | 1994 | 2.2–2.5 |
2. | BARI Barley-2 | 1994 | 2.0–3.0 |
3. | BARI Barley-3 | 2001 | 2.2–2.5 |
4. | BARI Barley-4 | 2001 | 1.75–2.0 |
5. | BARI Barley-5 | 2005 | 2.5–3.0 |
6. | BARI Barley-6 | 2005 | 2.5–2.75 |
7. | BARI Barley-7 | 2015 | 2.0 = 2.5 |
8. | BARI Barley-8 | 2018 | 2.2–2.51 |
9. | BARI Barley-9 | 2018 | 2.2 |
Modern barley cultivars developed by Bangladesh Agricultural Research Institute.
Source: Azad
Sorghum, one of the most drought-resistant crops that originated in equatorial Africa, grown for grain, fodder, fiber and/or biofuel, is the world’s fifth-most important cereal crop after rice, wheat, maize, and barley with 57.89 m t of annual global production in 2019 <http://www.fao.org/faostat/en/#data/QC>. In 1971–1972, the coverage and total productions were 1032 ha and 745 t, respectively which decreased to only 73 ha and produced 87 t in 2018 (Figure 5). The only recommended sorghum cultivar available in Bangladesh is BARI Jowar-1, the PGRC (BARI) has collected and conserved 268 sorghum accessions [21].
Area coverage and production trend of sorghum. Source: FAOSTAT 2020
Pearl millet, one of the earliest domesticated millets [2], is well-adapted to poor, droughty, and infertile soils and is, therefore, a vital subsistence crop in countries surrounding the Sahara Desert and in western Africa where soils are tough and rainfall is low <www.plantsoftheworldonline.org/taxon/urn:lsid:ipni.org:names:77105978-1>. In 2007–8, the coverage and total production of pearl millet in Bangladesh were only 26.72 ha and 35 t, respectively which increased to 28.7 ha and produced 38 t in 2018 (Table 9). However, it went completely out of cultivation in the subsequent year [4]. Only two accessions of pearl millet germplasm are conserved at the PGRC, BARI [18].
Year | Pearl millet | Proso- and Foxtail-millet | Other Cereals | Binnidana | ||||
---|---|---|---|---|---|---|---|---|
Area (ha) | Production (t) | Area (ha) | Production (t) | Area (ha) | Production (t) | Area (ha) | Production (t) | |
2007–2008 | 26.72 | 35 | 1770.44 | 1466 | 8110.12 | 5048 | ||
2008–2009 | 26.72 | 35 | 1251.01 | 1100 | 2618.62 | 1697 | ||
2009–2010 | 24.29 | 40 | 1214.57 | 1000 | 2024.29 | 2000 | ||
2010–2011 | 64.78 | 100 | 1214.57 | 1000 | 2429.15 | 2000 | ||
2011–2012 | 60.73 | 80 | 1214.57 | 1000 | 1619.43 | 1000 | ||
2012–2013 | 56.68 | 80 | 1214.57 | 1000 | 275.30 | 180 | ||
2013–2014 | 40.48 | 380 | 1214.57 | 2000 | 238.87 | 160 | ||
2014–2015 | 36.44 | 50 | 1214.57 | 7000 | 12.14 | 90 | ||
2015–2016 | 36.03 | 48 | 1214.57 | 1000 | 404.86 | 200 | ||
2016–2017 | 30.77 | 40 | 1214.57 | 1000 | 404.86 | 485 | ||
2017–2018 | 28.74 | 38 | 809.72 | 1000 | — | — | ||
2018–2019 | — | — | 809.72 | 1000 | — | — | 225.10 | 5 |
Proso millet is rich in protein, minerals, vitamins, and micronutrients; it is gluten-free and therefore, ideal for the gluten intolerant people. The nutritive parameters of proso millet are comparable to or better than common cereals [24]. Under drought and poor soil conditions, it also gives a better yield compared to all other crops, where there is a probability of complete failure of other grain crops [25]. Foxtail millet is an underutilized, drought-tolerant crop that stands to become much more important in a potentially much warmer and dryer future environment [26]. In 2007–8, the coverage and total productions were only 1770.44 ha and 1466 t, respectively which decreased to 809.72 ha and produced 1000 t in 2019 (Table 9). The only cultivar of Cheena, Tushar, and four cultivars of Kaon,
Finger millet and ditch millet are grown on a very limited area in the districts of Kushtia and Rajshahi; others are cultivated all over Bangladesh with little inputs in poor and marginal lands including the river beds [28]. Oats and rye are extremely nutritious, with a higher fat content than most cereals and an excellent grade of dietary fiber. In 2007–8, the coverage and total productions were only 8110.12 ha and 5048 t, respectively which decreased to 404.86 ha and produced 485 t in 2017 (Table 9). Moreover, on cultivation data/information was available for subsequent years [4], perhaps went to out of cultivation also. A new cereal crop “
Cereal (in fact rice) agriculture is synonymous with Bangladesh agriculture that plays a key role in food security and livelihood. Only 92 cropping pattern out of existing 316 was identified as an exclusive non-rice area which occupied less than 9 per cent of the net cropped area of Bangladesh [7]. The cereal agriculture, and agriculture in Bangladesh as a whole, is facing serious natural and man-made hitches that deserve special attention to this sector. The arable land is decreasing at an alarming rate (0.1% yr.−1) due to urbanization, roads and highways, infrastructure development, etc., severe degradation of natural resources like soil, water, climate, etc., the recurrent occurrence of devastating flood and drought, and the looming threat of salinity increment in the coastal region. Further in Bangladesh condition, the global climate change and related adverse effects on agriculture are rendering the worst impacts in temperature rise, abnormal rainfalls, sea-level rise, frequency of cyclone and storm surges, the encroachment of more saline areas, aggravation of drought problem and reduction in the availability of surface and groundwater [29]. There is a substantial extent of degradation of agricultural lands caused by soil erosion (1.70 m ha), river erosion (1.70 m ha), soil fertility decline (8.00 m ha), depletion of soil organic matter (7.50 m ha), waterlogging (0.70 m ha), soil salinity (0.84 m ha), pan formation (2.82 m ha), acidification (0.06 m ha) and deforestation (0.30 m ha) [30]. Some other soil-related constraints to cereal crop production are heavy consistency, poor structure, high osmotic pressure or drought, both physical and physiological, causing a reduction in the ability of plants to absorb water and nutrients, etc. The soil health scenario becomes worsen due to imbalanced fertilization and unplanned increase in mono-crop based cropping intensity and thus, the quality agricultural land is getting scanty. A survey reported that 2% of arable land belongs to a very good type, 34% good, 39% moderate, 16% poor, and 9% very poor [30].
The quality and timely supply of agricultural inputs are other constrain for cereal crop cultivation in Bangladesh. For instance, about 18% of the entire seed requirement of the country can only be met from certified and truthfully labeled seeds of Government and private sources, and the remaining 82% comes from the seed storage of farmers’ own. There are serious problems in the quality of seeds supplied by public, private, and farmers themselves [30]. The scarcity of irrigation water (and its resources) is added to another constrain for sustainable cereal production in Bangladesh and the world as well. For example, an increase in Boro growing area in Bangladesh to 6 m ha by 2050 will increase the irrigation demand to
Bangladesh, one of the highest densely populated country in the world, endures the 8th largest world population (
Due to climate change, sea-level rise will cause inundation of about 16% of total cropped area, displace 10% of the population, increased salinity in the coastal zone and reduce crop yields, ultimately causing loss of 2 m t of crop harvest [29]. Global warming will cause cyclones and storm surges in high frequency and volume. Due to river erosion and storm surges, moderate to severe erosion will occur in flood plains and char lands. Out of 2.85 m ha, about 1.00 m ha in the coast is affected by different degrees of salinity which will continue to increase due to climate change. About 2.32 m ha and 1.2 m ha of net cropped area are respectively severely and moderately drought-affected and the problem will further aggravate. Moreover, about 1.32 m ha and 5.05 m ha of the net cropped area are, respectively severely and moderately flood-prone that seriously hamper crop production [29]. Besides, reduced availability of surface and groundwater in the dry season due to excessive extraction of groundwater for irrigation purposes is coming up as a serious problem. The development of water-saving techniques in agriculture is a critical issue. The inadequate facilities and programs for the production and distribution of quality seeds and other inputs to the farmers are the main reason for low productivity; there is a wide yield gap between demonstration and farmers’ field yield. For example, the current yield gaps between demonstration and farmers’ yield for Aus, T. Aman and Boro are 2.74, 4.89 and 4.08 t ha−1, respectively [29]. Thus, the challenge is to reduce the current yield gap for cereal production enhancement. The farmers’ knowledge-gap in adopting modern agricultural technologies also leads to low productivity. Further, the yield ceiling of modern cultivars needs to be improved by developing super cultivars. Low quality and adulterated agricultural inputs
Although Bangladesh faces huge constrains and challenges in achieving food and nutritional security due to its high population, diet changes, and limited room for expanding cropland and cropping intensity, Bangladesh will remain self-sufficient in rice at least to 2050 at the present rate of technological, in both cultivar and management, advancement and population growth [16]. The Intergovernmental Panel on Climate Change estimates, on contrary, reported that the rice production in Bangladesh could decline by 8 per cent and wheat by 32 per cent due to higher temperatures and changing rainfall patterns by 2050 [33]. For achieving food and nutritional security in the coming days, the following actions may be taken to increase cereal grains yield (per unit area) and production –
Minimize the yield gap by (i) increasing actual farmers’ yield corresponding to current yield potential (Yp) levels by improving the crop management practices,
Develop new cultivars with greater yield potentials and stress tolerance.
Replacement of current low yielding cultivars with and other recently released high yielding hybrid, short-duration and fast-growing, drought and salt resistant cultivars. The expanded availability of modern rice and other cereal crops cultivar(s) could endure climate change impacts without yield penalties [16].
New climate-smart agriculture/farming technologies e.g., climate-resilient (modern) cultivars for stress-tolerance, profitable location-specific cropping patterns, conservation agriculture, innovative cultural management to minimize yield gap, mechanization, etc., to be developed to grow four crops in a year (in the same piece of land) including three rice crops, and to bring unfavorable agro-ecosystem under productive sustainable agricultural practices.
A decrease in the dependence on groundwater by increasing surface water use for irrigation purposes, and replacing rice with wheat or other crops that use less water. Sustainable groundwater use in some areas combined with the use of more surface water (through rubber dam, sluice gate, flash gate and dug well) and moving some production to other less intensively cultivated areas will help meet this challenge. For example, barley is a stress-tolerant and saline adaptive crop [35]. Barley is best suitable as a Rabi (Winter; Mid-October to Mid-March) crop to cope with the saline-prone south coastal region of Bangladesh.
Millets
Stress-tolerant minor cereal cultivars generally possess poor yield potential; the development of high yielding cultivars would be a climate-resilient technology to secure food and nutritional security in the changing climate.
Skill development of farmers, extension workers and researchers through appropriate training programmes.
Promote farmer’s rights through documenting farmer’s indigenous innovations, farmer’s creativity under plat variety and farmers’ right protection act and establish a database for indigenous technologies owned and practised by the rural farming community [37].
Overall, the GAP (good agricultural practices) and SPS (sanitary and phytosanitary) measures will have to be popularized and promoted.
Value addition to cereal grains and by-products ensures the nutritional and economic security of farmers and the economic growth of the country as a whole. For example, producing breakfast cereals, multigrain flours, bran oils, syrup, starch, health-foods, animal feed, nutraceutical/pharmaceutical products, substrates for (oyster) mushroom (
The major cereal grains,
Author thanks, Professor(s) Dr. Md. Solaiman Ali Fakir and Dr. Md. Habibur Rahman Pramanik, and anonymous reviewer(s) for their valuable comments, constructive criticism and/or improvement suggestions.
UV VIS Absorption spectrophotometer (applicable for coloured liquid) and UV VIS reflectance spectrophotometer (applicable for flat samples of coloured solids) are the two major equipment now being used in colorimetric evaluation related labs for textiles and other industries. Colour measurement of liquid dye solution or colorimetric titrations is known with the advent of UV Vis Absorption spectrophotometer. Colour measurement of solid substances was quantified by CIE internationally in 1923, which was further revised in 1976 and is continuing [1, 2, 3, 4]. Colour matching theory was made commercially applicable in 1950s. Till then, so many varied applications of colorimetric evaluations have made precision process control and product control possible for coloured textiles.
where,
where
So, combining these two laws, called Beer-Lambert Law [3, 4]:
where
In simple colorimetry, the entire visible spectrum (white light) is used to pass through the solution and consequently the complementary colour of the one absorbed, is observed as transmitted light. In UV VIS absorbance Spectrophotometer, a monochromatic light or a narrow band of light radiation is used, replaced the colorimeter and then this instrument is called Absorbance spectrophotometer or reflectance spectrophotometer, differentiating by measurement parameter i.e. measuring as absorbency or optical density of transmitted light intensity for colored solution.
Limitations and Cares for measuring absorbance/optical density parameter of liquids:
Beer-lambert law does not hold good for a concentrated solution. So sufficient dilution is necessary to obtain correct and reproducible results. Dilution to 50 to 100 times is preferably used.
Beer-lambert law does not hold good, if the solute/coloured liquid under measurement, has ionizing, dissociation or aggregation/association tendency or complex-forming tendency in the solution.
Example: Benzoic acid in Benzene solvent form dimer, i.e., aggregates as dimer, and Potassium dichromate on higher dilution, dichromate ions are ionizing or dissociating into chromate ions, which are the causes of deviation of correct reading in both these two cases.
If the colour liquid has fading tendency with time, then the sample is faded away due to instability of coloured molecules/solutes and hence incorrect results are obtained.
Presence of any impurities like fibre dust, residual dye bath additives/electrolyte etc. (comes to the coloured liquid solution during extraction of coloured substance/dyes/pigments from a dyed textiles or during measuring residual coloured liquor of exhausted dye bath effluent) and causes incorrect result.
Use of electrolyte at higher concentration usually shift the λmax values and changes the extinction coefficient/coefficient of absorption etc. and hence occur deviation in results.
Presence of any additives changes/makes alterations in the refractive index values of the coloured solution and hence it gives wrong results.
Changes in pH of solute/coloured liquid causes deviation in results.
While for Solid coloured samples, surface reflectance values are measured for any solid-coloured substance, the measurement parameter is reflectance (R values at different wavelengths user-chosen wavelength or preferably at maximum absorbance wavelength, i.e., λmax) and the instrument used for R values of solid coloured substance, is called UV-VIS Reflectance Spectro-photometer.
Thus, when it is required to measure colour from a solid dyed/printed surface, the measurement parameter is not absorbancevalues, but is Reflectance values (R), i.e., reflected light intensity from a solid surface of dyed textiles/coloured/coated polymeric film/plastics etc.
Besides surface colour strength, the colour difference and other colour interaction parameters [2, 3, 5, 6] like Total colour differences (DE), Lightness/Darkness (DL*), Red-ness/Green-ness (Da*), Yellowness/Blueness (Db*), Changes in Chroma (DC) and Changes in hue (DH) can be calculated by CIE formulae [1, 2, 3, 4]. Also, non-coloured surface appearance properties of any flat sample including textile fabrics can be determined easily in terms of whiteness index, yellowness index, and brightness index values using appropriate and respective formulae of CIE/ASTM or other standards [1, 2, 3, 5, 6] to compare any changes in its surface texture for any chemical treatment or physical intervention on the sample, which is very useful for industry.
Limitations and Cares for measuring Reflectance/Surface Colour parameters of solids:
Some special cares needed during measurement of colour values of solid dyed textiles
If the dye uniformity is not up to the acceptable level, the measurement of K/S values will vary a large resulting higher coefficient of variation of K/S data for non-uniform dyeing i.e., Un level dyeing (in general more than 5% coefficient of variation of K/S data is taken as un-level dyeing for all type of dyed textiles).
During mounting of solid coloured textile yarns or fabrics, background opaqueness of the sample is to be assured for correct results (So, nos. of folds required in the sample to obtain opaqueness, are to be pre-decided and to be kept constant in all the measurements).
During mounting of solid coloured textile yarns or fabrics, changes in the sample orientation (warp wise or weft wise vertical or horizontal measurement or changes of side of the textile fabrics (Colour value in one face of fabric usually differ from other face) differs colour strength values. So, warp or weft wise orientations/and face or backside facing measurements of colour values are to be pre-decided and not to be changed throughout all the colour value measurements of all samples to compare.
Some chemical/biochemical treatment before dyeing may alter the surface texture and hence changes scattering value of the sample and hence deviations in K/S value measurement occurs. So, care should be taken to avoid such treatments which changes texture of the sample and alter K/S values to a large extent.
Defects in fabric (Any defect of the fabric on surface may cause variation in colour value) like Slabbing/snarls, patchy dyeing, dyeing warp or weft bar etc. causes such variations. So defective fabrics must be avoided during measurements of surface colour values.
Different types of selective colorimetric evaluation methods are described one by one in brief with examples/case studies with experimental data below mentioning the importance of each method.
An optical UV-VIS absorbance spectrophotometer records the absorbance values at different wavelength range at which absorption occurs, together with the degree of absorption at each wavelength and thus a pictorial curve of wavelength (X-axis) vs. Absorbance (Y-axis) called UV-VIS spectrum of that solute from its very dilute solution (preferably 1/100th dilution). The resulting UV VIS spectrum is presented as a graph of absorbance (A) versus wavelength showing maxima (λmax) and minima (λmin) of absorbance at different wavelength in both UV and visible region.
Solute molecules absorb ultraviolet or visible light from a monochromatic beam of incident light beam and the rest are transmitted through the solutions of fixed path length (b or d) in a cuvvete/quartz cell holding the sample solution. As optical density or absorbance is directly proportional to the Path length,
Different solute molecules absorb UV-VIS light/radiation of different wavelengths depending on its chemical nature and structure with or without interference, if any, as depicted by corresponding absorption spectrum showing absorption peaks and troughs/bands according to the chemical structural groups present in the respective solute molecules present in the coloured dilute solution. Thus, the UV-VIS spectral scan (For absorption or optical density) of a particular-coloured compound/dyes/pigment at a particular wavelength (at λmax) is deterministic and identifiable instrumentally, which is the basis of the identification and estimation of purity/concentrations of any colourants/dyes/pigments by UV VIS spectrophotometric (Absorbance) evaluation.
CASE STUDY 1: As a case study, UV–VIS Absorbance spectrophotometric method of determination of purity and concentrations of rubia/madder as a natural colorant is discussed:
Calibration curve (Figure 1) is prepared by using 1,2,3,4,5,6 to maximum of 10 mg of natural
Calibration curve for the
Once the calibration curve is ready in UV VIS absorbance spectrophotometer screen or manual graph paper, the unknown solution of the same compound having unknown quantity of solution is placed in UV VIS scanning taking 10 ml of sample solution of unknown concentration, diluted to known times i.e. say 50 to 100 times until a very fent colour appears in the solution and from that dilute solution 2–3 cc is poured in quartz cell of sample solution and mounted in UV-VIS absorbance Spectro, to measure its Optical density/absorptivity values. Once the absorptivity/Optical density values of sample of unknown concentration are obtained, the concentration can now be easily obtained by putting measured absorbance or OD (optical density) values in calibration curve of Figure 1, to find the Concentration/purity of the content of rubia/madder coloured component in it in specific unit, after correcting the value with dilution factor and converted into proper unit like % or g/lit etc. as per requirement.
CASE STUDY 2: Identification of Natural dye Madder/
The two red dyes-- [(i) Natural Rubia (Manjishtha/Madder) which contains manjisthin (similar to alizarin as coloured compounds) in its natural extract and (ii) synthetic alizarin coloured compounds as synthetic same coloured dye] were weighed separately (0.1 gm) and dissolved in 1000 ml dichloromethane/methanol and then wavelength scan under UV-Visible absorbance spectrophotometer was taken for both. For visible spectral analysis, this solution may be used, but for UV spectral analysis this solution needs to be further diluted by 5−10 times for better results. Comparative Identification of Synthetic alizarin and madder (Rubia) as natural colourant/dye, by this UV VIS spectral analysis, involves a comparison of the minute details of UV–VIS peaks/bands of UV VIS spectrum (at λmax) of
UV spectrum of
Thus from Figure 2 Comparative analysis of UV-Vis Spectrum of Natural Rubia/Madder extract and Synthetic alizarin (as shown in Figure 2 indicate that Natural
Specific Wavelength (nm) and Sample | Peak Reading at Specific wavelength (nm) | Results with inference (describing difference in UV VIS spectra between the two samples taken) | |
---|---|---|---|
For Synthetic Red Alizarin | For Natural | ||
250 nm (1.38 OD) and 426 nm (0.309 OD) | — | The pattern of the peaks in UV and Visible region are very different for the two samples | |
— | 250 nm (0.954 OD) and 491 nm (0.171 OD) |
UV VIS spectral peaks analysis of natural
[Source-IS standard- 17,085: 2019 [9]].
Individual UV VIS absorbance Spectrum at visible region only at 390–700 nm, when is partly enlarged for 390–450 nm, it is also observed that the UV–Vis absorbance spectrum of aqueous solution of natural Rubia/Madder extract (extract of Indian Madder i.e., natural manjishthin) also shows small hump like peaks at 398 nm (with 0.801 OD) and also indicating large hump like peak at also 426 nm (with 0.838 OD), which are vivid from Figure 3. Therefore, the appearance of the above said two respective peaks in the said wavelength region lead to indicate the presence of natural Rubia/Madder with manjisthin (not synthetic alizarin), which is more clearly understandable in the enlarged peak of highlighted part of UV–VIS Spectrum (Figure 3) of extracted solution of
Part of enlarged UV-Vis Spectrum of Natural Rubia/Madder colorant (containing manjisthin).
Optical density/absorbance at λmax for extract of natural
Colour quantification in mathematical term is necessary to develop a systematic understanding of the principles of colour perception and measurement for understanding the differences between colours of two samples i.e., match and mismatch for any method of colour encoding/imaging and communications, to give a more realistic picture for colour reproduction. Hence, TRISTIMULUS VALUES (X, Y, Z) are defined as three coordinates to define any colour for communications, where X, Y and Z values are as follows:
Thus, Tristimulus values X, Y, Z can be calculated from measured total reflectance of a textile or similar flat surface with its derivative formula function as shown below [2–3, 6–8]:
where Pλ=Spectral power distribution of standard source, Rλ = Spectral reflectance of substrate and xλ. yλ. zλ=colour coordinates/factor of standard observer for red, blue and green.
For ease of working, colours are redefined from TRISTIMULUS values to CIE Chromaticity coordinates (x, y and z instead of Capital X, Y, Z as tristimulus values), which can be plotted in two-dimensional plot. These new CIE chromatically coordinates (x, y, z) can be defined as follows.
From Eq. (22), i.e., x + y + z = 1, the value of anyone CIE chromaticity coordinate can be determined from the values of other two CIE chromaticity coordinates, i.e., the third one can be determined easily from first two.
Still, as Plot of Dye Concentrations Vs Reflectance (R) are non-linear and non-additive, Tristimulus values X, Y, and Z are interdependent on one another, and CIE chromaticity coordinates are still two factors dependent variables to get third one. HUE, value chroma are also 3 coordinates based, it is difficult in practice to control all those multivariate/factors/colour parameters simultaneously to get a precision match of colour.
So, quantification of colour was finally made by Kubelka and Munk [2, 3, 6, 7, 8], where K/S value (surface colour strength) is defined as follows:
where K is the coefficient of absorption; S, the coefficient of scattering; and Rʎmax, is the Reflectance value at maximum absorbance wavelength (λmax) and CD is the dye concentration and α is the constant. Moreover, K/S Vs Dye concentration plots are linear, and K/S is additive in nature.
For Additive nature of K/S value, for use of mixture of colourants/dyes at different concentrations c1, c2 and c3 respectively for dye1, dye2 and dye3 K/S values of resultant fabric may be written as:
Thus, handling of K/S values become much easy to match colour, as because K/S is treated as a single variable i.e., it operates on a single constant theory (scattering remaining constant for same fabric and dye sample) and K/S is directly proportional to dye concentration in linear and additive relationship.
For dyed textiles/clothes, it is pre-assumed that dyes on specific textile fabric do not add or substract, i.e., change scattering and K is the sum of absorption of dye stuff on dyed textiles and therefore, it is only dye absorption values of dyed textile substrate (if textile substrate remained unaltered/fixed). So, it may be considered that for dyed textiles, K/S directly varies with concentration of dyes linearly and scattering of dyed textile substrate is independent of dye concentration (which is not the case for pigments in paints for wall colours). So, in textile it is single constant theory of colourmatch prediction through K/S values, as most widely applicable colour parameter for colour quantification, measurement and colour matching of textiles. So, for the particular dyed textile sample (with same fibre material, yarn parameter and fabric construction/surface finish remain unaltered) scattering value is assumed to be constant.
Thus, higher is the K/S value, meant higher is the dye absorption in textiles, meant higher absorption value of dye thus signifying or indicating higher dye uptake, but this measurement is surface colour strength, not bulk dye uptake, which can only be determined by extraction of colour from dyed textile samples and then analysis of optical density or absorbance values in absorption spectrophotometric analysis of coloured liquid.
Dye Uniformity in terms of CV % of K/s values at minimum 10 different points may be expressed for deciding factor for level/unlevel dyeing. CV % of K/S values within 5% value is considered as acceptable for level dyeing and more than 5% values (CV % of K/S values) is considered as un-level dyeing leading to rejection of the sample.
Colour attributes of a human perception consisting of any combination of chromatic and achromatic content in terms of differences in combination of red, blue and green sensation of human eye (as shown in Figure 4) alters change in predominating hue which can be described by chromatic hue names such as yellow, orange, brown, red, pink, green, blue, purple, etc., or by achromatic colour names such as white, grey, black, etc., and is associated with some other attributes like bright, light, dark etc., hence colour differences in between two samples arises by value of these attributes of human perception or instrumental measurements. Measurement of colour differences is important for judging two nearer coloured samples as match with degree of matching or mismatch. It is Judged by differences in light and dark (∆L*), Redness or Greenness (∆a*) and Blueness or yellowness (∆b*) as CIELab* colour difference coordinates in CIE colour difference space diagram to determine the total colour difference values (in terms of ∆
Red-Blue-Green perception of colour.
Thus, according to CIE (Commission International de eclairase, Paris) 1976, total colour difference values (in terms of DE* or ∆
The above said terms DE* or ∆
CIE L*a*b* colour difference space diagram. By human eye (wavelength vs. intensity).
The above said CIE colour differences equations are depicted below for ease of understanding:
where,
Chroma, (psychometric chroma) values in CIELAB colour space can be calculated as follows:
where,
CIE 1976 metric Hue-Difference (ΔH) for CIELAB system can be calculated as follows:
Moreover, Brightness is another additional colour attribute associated with perception of colour differences. This attribute of visual sensation of colour gives an additional visual perception that appears to be more or less intense or luminescence i.e., this visual stimulus appears to emit more or less light from specific hue of colour and differs from one another.
Brightness Index (BI) as per ISO-2469/2470–1977 method [11] can be calculated by following ISO formula for this:
Application of fluorescent brightening agents to white textiles show an additional higher reflectance value more than 100 and up to 150. Though the sample appears to be still whiter as usual, there is emitting of more reflectance of incident light in the bluer zone and the appearance thus changes its chroma towards blue increasing its more whiteness and brightness, where brightness value may be represented or expressed in quantitative term by ISO standard method. Conversely, yellowing of white textiles by chemical treatment or by heat scorching or by any type of degradation by exposure to light or by gas fading etc. can blur the brightness value of the white or dyed sample. Thus, along with colour differences like DE, DL, Da, Db, this Brightness index (BI) as another additional colour attributes related to surface appearance properties of textiles have immense important role and simply high or low BI values an important colour surface appearance parameter too in defining the colour quality of any textile fabric.
A recent newer concept of defining colour differences by Colour Difference Index (CDI) values as a measure of dispersion of colour values at different points from all angle of instrumental measured variation, depending on dyeing process variables, to understand the combined effects of different dyeing process variables by a single parameter, is defined [12] taking only the magnitudes of the respective Δ
Higher the differences in between maximum and minimum CDI values, higher is the dispersion of colour values at different points i.e., colour values are more widely dispersed, and that variable become critical for reproducibility for such dyeing. So, lower the differences in between maximum and minimum CDI value in one set of dyeing for particular dyeing process variables or use of mixture of same set of binary mixture of dyes, better is the match with lower dye dispersion in such cases of colour match CDI value below 5 is acceptable and good and below 1.0 is considered as excellent.
CASE STUDY 3:
The above shown data in Table 2 on colour parameters, obtained in a study on use of different mordant concentration yields different surface colour strength(K/S) showing reasonable differences of Colour values in terms of ∆
Mordant Concn. (%) | ∆ | ∆ | ∆ | ∆ | ∆ | ∆ | MI (LABD) | CDI | |
---|---|---|---|---|---|---|---|---|---|
5 | 2.24 | −12.52 | 3.77 | 8.25 | 8.95 | −1.49 | 23.35 | 1.19 | 3.26 |
10 | 2.65 | −15.61 | 4.29 | 8.24 | 9.07 | −2.00 | 22.87 | 1.34 | 3.75 |
15 | 3.89 | −20.90 | 4.73 | 7.53 | 8.56 | −2.42 | 17.97 | 2.11 | 2.41 |
20 | 3.15 | −16.51 | 4.84 | 12.09 | 12.92 | −1.64 | 18.06 | 1.69 | 1.35 |
25 | 2.45 | −12.90 | 4.53 | 10.81 | 11.60 | −1.68 | 20.57 | 1.54 | 1.94 |
Effect of Mordant concentration on Colour Strength and Colour Differences for dyeing silk fabric with tesu (containing butein) extract as natural colourant.
Colour matching of two samples are considered as fully satisfactory, if any one of the following 3 conditions are achieved with plus-minus mutually accepted tolerances values of their colour differences in CIELab attributes as follows:
Thus, to become colour of produced sample = colour of given standard sample, following should be the conditions be satisfied - i.e., below given conditions (1)–(3).
(XSL, YSL, ZSL) values of produced sample = (XSD, YSD, ZSD) values of given standard sample where X, Y & Z are the tristimulus value of Sample (SL) and Standard (SD)
(Reflectance)SL value at 400 to 700 nm of produced sample = (Reflectance)SD value at 400 to 700 nm of given standard sample
(K/S) SL value of produced Sample = (K/S) SD value of given standard sample, where K/S = α C.
3rd Conditions are easy to check and achieve, as it is additive in nature and Dye Concentration vs. K/s values plot are linear and is predictable from sample database by computerised algorithm.
For computer aided colour matching theory [2, 6, 7, 8], for a shade from mixture of multiple colourants (say 3 colourants), following three equations are to be solved as a function of dye concentrations of the colourants (1, 2,3 or n) and to be checked by measuring tristimulus values or reflectance values or K/s values with measurement of DE*, DL*, Da* and Db* values under different standard illuminants.
where x, y, z tristimulus values of standard given sample are to be matched with the matched dyed textile sample to be produced, by using say 3 different dyes with respective concentrations of those 3 selective dyes indicated by c1, c2 and c3. For determining/predicting these selective concentrations of specific dyes to get a specific match of colour, In practice, the reflectance values of standard sample at 400 to 700 nm are initially measured from standard dyed textile substrate and those reflectance data are processed through computer aided software to generate matched K/S Values, within tolerance set for specific L, a and b colour difference parameters and DE total colour difference parameter to match for the predicted/produced sample. As K/S values vs. concentration of dyes is linear & additive, so this is used as basic data for handling colour match prediction by computer aided colour measuring cum matching instrument from different companies with application software in built in the system.
Colour matching is always associated with Some practicable values of DE*, DL*, Da* and Db* values, within acceptable tolerances, but is also associated another factor/term called metamerism index (MI), due to measurement of colour values under different conditions of measuring colour values i.e. within varying illuminates or varying observers or varying instruments etc. [2, 6, 7, 8].
Thus, only colour difference values do not represent true differences of perceived colour in human eye due to observer’s metamerism or even instrumental metamerism or illuminate metamerism etc. An ideal or perfect colour match is called isomeric match i.e., which are always match under all illuminates or under all observers or under all instruments in all the ranges of wavelength values in visible region and then that ideal match is called true isomeric match. While Most of the given standard of colour and produced samples are not at all show isomeric match, there is always some differences in their colour difference results at different wavelength range or otherwise i.e. when two coloured sample (standard and produced sample for colour matching) show match under one illuminant/one observer or one instrument but do not match under any other illuminant/other observer or other instrument at different wave length values is termed as a metameric match. So, it is a challenge to produce a Least metameric match instead of ideal isomeric match. A general metamerism index (MI) value can be calculated using Eq. 23, as follows:
where ∆R = Difference in reflectance between pair of metamer samples;
The Metamerism-Index (MI) indicate the probability of any two near match or matched two samples when show the different colour difference values under changed conditions of measurements like if measured under two different illuminants (represented by the first and second illuminant) or under two different make reflectance spectrophotometer instruments or under any other two different conditions of measuring colour parameters of the said two specific samples by calculating. CIE LAB i.e., LABD metamerism index [2, 6, 7, 8], which is represented below in Eq. 24:
∆
If MI is low, the colour difference between the sample pair is the closer and more similar for different conditions of measurement, even under different illuminates or observers or instruments. So, matching of two-coloured samples produced at comparable conditions are to always to minimize to obtain least metameric match for control of colour by using computer aided colour measuring and matching system [7, 13].
CASE STUDY 4: Computer aided colour match prediction for dyeing of textiles: as an Example
Practical Guideline for Colour Match prediction: it is necessary to prepare Company wise Dye Class type and Sample type (Substrate fibre type) database by calibration dyeing [7, 14, 15] of 0.25, 0.50, 0.75, 1.00, 1,25, 1.50, 1.75. and 2, 2.5, 3 percent dyed sample of specific fabric (based on type of fibre) i.e., say- bleached cotton fabric and their reflectance or X, Y and Z Data are to be measured and to be saved as library of database for use for formulation prediction of dye weight % required for colour matching from time to time for given standard sample.
Colour matching tolerances against Standard daylight D65 illuminate, Artificial Tube light -TL84 (A) and fluorescent light (F) are to be set as maximum 1.00 for each light or to be mutually fixed between buyers and sellers in order agreement. If dye cost from lot to lot regular purchase is updated in this system, cost of dyes for different formulations are also calculated and available at fingertips, other dyeing process and utility cost remaining same. Not only it helps to reduce dye inventory and it saves matching time for lab to production trial time with reasonable known combination of dyes and cost involved along with average predicted dE*, dL*, da*, db* values to know the degree of precision of colour matching, below is the example of one colour match prediction formulation using computer aided colour matching system with database for different class of textile dyes already fed in (the present example is colour matching formulation of cotton fabric with reactive dyes database, as given in Table 3.
Standard Id = Coloured Cotton Fabric-C 12 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
RFl DATA For Std. | 3.65 | 3.90 | 4.46 | 5.87 | 7.44 | 8.77 | 9.32 | 10.61 | 11.56 | 12.71 | ||
12.33 | 11.14 | 10.33 | 9.34 | 8.10 | 7.82 | 6.98 | 6.53 | 5.39 | 4.34 | |||
Dye class used | = Reactive Dye database for white cotton | |||||||||||
Dye ID# used | 1,3,4, 6, 7,9,10, 11and 12 from data base | |||||||||||
Substrate ID# | 3, Enzyme pre-treated Bleached Cotton | |||||||||||
TOLERENCES | dE* for D65 Light = 1.00 | dE* for Artificial Light = 1.00 | ||||||||||
dE* for Fluorescent Light = 1.00 | ||||||||||||
ID# Colorant | Amount | Per cent | da* | db* | dL* | dE* | Rs | |||||
Matching Formulation Generated by computer Aided Color Matching System | ||||||||||||
Formula#1 | ||||||||||||
3 | R Red M3B | 0.15 | 0.15 | D | −0.0 | −0.0 | 0.0 | 0.0 | 56.51 | |||
6 | R Brown 5R | 0.68 | 0.68 | A | −0.42 | 0.2 | 0.10 | 0.23 | 227.73 | |||
10 | R Procian Blue 2R | 1.41 | 1.41 | F | −0.62 | 0.4 | 0.33 | 0.66 | 495.00 | |||
2.14 | 2.14 | 778.24 | ||||||||||
Formula # 2 | ||||||||||||
3 | R Red M 6B | 0.13 | 0.13 | D | −0.0 | −0.0 | 0.1 | 0.01′ | 48.53 | |||
11 | R Grey 2R | 0.54 | 0.54 | A | −0.52 | 0.3 | 0.0 | 0.36 | 188.26 | |||
10 | R Procian Blue 2R | 1.28 | 1.28 | F | −0.71 | 0.5 | 0.11 | 0.78 | 449.36 | |||
1.95 | 1.95 | 686.15 |
Example of a colour match predicted from the database of direct dye for cotton.
Thus, the above predicted 2 formulations indicate that formaulation#1 is less metameric as understood from comparison of their dE*, dL*, da*, db* values, and cost wise Formulation#2 is least cost match,
Compatibility between any two same class of dyes can be judged by different methods, such as (i) comparative subjective visual assessment of the degree of on-tone build up by carrying out a series of dyeing for both dyes to same substrate and checking gradual colour build up by visual assessment, (ii) theoretical prediction of compatibility [16] by comparison of rates of dye by rate of diffusion of dyes by determining diffusion coefficients or by determining time of half dyeing for each individual dye at comparable dyeing conditions (iii) by quantitative assessment of change in hue angle(∆H) for increasing dyeing time and temperature or increasing dye concentrations [16] under two sets of dyeing for colour built up on specific textile substrate (iv) by comparing the nature of plots of ∆C vs. ∆L or K/S vs. ∆L values for two sets of progressive built up shades as said in point -no (iii) obtained by dyeing with varying dye concentration and also with varing dyeing time and temperature as said in point no-3 using 50:50 of two dyes [17] and (v) quantitative compatibility rating for the mixtures of more than two dyes by colorimetric analysis of actual colour strength developed (not on the basis of dye absorbed) for mixture dyeing in different proportions following Relative compatibility rating (RCR) method [12] by calculating differences of CDI (Colour difference Index) values [17, 18] as a newer empirical index of overall colour differences for dyeing different proportions of two dyes of different pairs of synthetic or natural dyes applied on any textiles.
CASE STUDY 5: Comparison of compatibility of two dyes by comparing the nature of plots of ∆C vs. ∆L or K/S vs. ∆L values for two sets of progressive built up shades by dyeing with variation of dye concentrations (SET-1) and dyeing with variation of Time and temperature (SET-2) using 50:50 of two dyes as well as also Determining compatibility of 2 dyes by Relative compatibility rating (RCR) method by calculating differences of CDI values for dyeing different proportions of any two dyes.
Dyes Selected are: Direct dyestuffs (make: Atul Ltd. (Tuladir)) of four different colours, i.e., Direct Turquoise blue (CI Direct Blue 199), Direct Red (CI Direct Red 31), Direct Yellow (CI Direct yellow 44), Direct Green (CI Direct Green 513).
Dyeing carried out for Conventional methods of determining compatibility, for obtaining plots of ∆C vs. ∆L or K/S vs. ∆L values for two sets of progressive built up shades following selected binary pairs (50:50) of synthetic direct dyes were applied on the 6% H2O2 (50%) bleached Jute fine hessian fabric using three pair of following combination of binary pair of direct dyes such as M-11 -Direct Red + Direct Green, b) M-12-Direct Red + Direct Yellow and c) M-13-Direct Red + Direct T. Blue taken in 50:50 ratio in two sets.
In Set I, the progressive depth of colour was gradually built up by varying dyeing time and temperature profile for each pair of dyes (M11, M-12 and M13), three jute fabric samples were dyed laboratory beaker dyeing machine with temperature controller for 10–60 min varying dyeing time period. The said dyed fabric samples were one by one taken out from the respective dye bath at equal interval of 10 min from dyeing temperature of 60°C onwards up to 100°C, maintaining the constant heating rate of 2–5°C/min. The final and ultimate dyed sample was taken out from dye bath after 60 min dyeing time at 100°C dyeing temperature.
In Set II, the progressive depth of shade was obtained by varying total concentration of dye mixture in 50:50 ratio but varying percent application from 20–100% of 1% shade for each pair of dyes, for 3 separate samples of jute fabrics, which were dyed at the at the increments of 20% points of dye concentration at pre-fixed dyeing conditions at 100°C for 60 min. Taking two dyes in equal proportions (50:50).
The colour difference values in terms of ∆E* and ∆L*, ∆a*, ∆b* and ∆C* for all the above said dyed fabrics using Set I and Set II conditions, against undyed fabric sample as standard for reference, were obtained by individually separate measurement of the colour difference parameters Using UV–VIS reflectance spectrophotometer within built software and computer attached. The compatibility of a selected pair of dyes was judged [16, 17, 18, 19] from the degree of closeness and overlapping of two curves ∆C vs. ∆L or K/S vs. ∆L observed using the two sets of dyeing (Set I and Set II) as shown in Figure 6.
Plots showing K/S Vs ∆L curves of (a) M11-D Red: D Green (b)M-12 -D Red: D yellow and (c)M-!3 -D Red: D T. Blue for two sets of each showing M-12 -D Red: D yellow combination has good compatibility, while M11-D Red: D Green combination ahs not so good compatibility or has fair compatibility and M-!3 -D Red: D T. Blue has more or less average compatibility at higher time (
Thus, both of these methods show a similar results, while the method −2 of RCR compatibility rating method is easier and less time consuming and hence has advantages over plotting of K/S Vs DL.
Dyeing of any textiles, say cotton or jute or any other fibres to be dyed with specific class of synthetic dyes like reactive dye (or even for any natural dyes) need to be optimised [14, 15] to derive standard dyeing conditions to obtain maximum surface colour strength (K/S values).
So, it need to have experiments on varying dyeing time, temperature, dye concentration, salt concentrations, MLR and pH etc., so that reproduced and uniform dyeing can be achieved easily.
However, for reactive dyes, Dyeing time has two type -Dye exhaustion time and Dyeing fixation time and similarly dyeing temperature has two dimensions, i.e., Dye exhaustion temperature and Dye Fixation temperature and also for last stage of alkali fixation of reactive dye, addition of soda ash is to be considered, also, besides addition of salt for exhaustion as evident from earlier references [20].
UV VIS reflectance spectrophotometer thus helps by colorimetric analysis of Surface colour strength and other colour parameters, for dyeing of any fibre with specific class of dye by varying conditions of dyeing.
|CASE STUDY 6: Optimization of dyeing process variables for jute dyeing with reactive dyes.
Fabric used: 3% H2O2 bleached fine hessian jute fabric having 215 tex jute yarns as warp and 285 tex jute yarns as weft, 64 ends/dm and 58 picks/dm, fabric area density 320 g/m2 and fabric thickness 0.70 mm, obtained from M/s Gloster Jute Mills Ltd., Bauria, Howrah, was used.
Dyes Selected: (i) Hot brand Reactive Green HE4BD (CI Reactive Green 19), (ii) Hot brand Reactive Orange CN (C.I. Reactive Orange 84) and (iii) Cold brand Magenta (C.I. Reactive Red 11) were used.
Measurement of Colour Parameter: K/S values of differently dyed jute fabrics under varying conditions of dyeing were determined by using computer-aided UV VIS Reflectance spectrophotometer [Premier Colour Scan Instrument Ltd. Mumbai Make Model SC 5100A] along with associated Colour-Lab plus software employing Kubelka Munk [2, 6, 7, 8] equation and CIE-Lab equations against a particular undyed (bleached) sample set as standard followed by calculating the K/S values with the help of relevant software.
The relevant color parameters measured for each sample of varying dyeing conditions are detailed in Table 5 and plots of each dyeing process variables vs. K/S values are shown in Figure 7 for 3 selected reactive dyes applied on jute under varying conditions of dyeing, to optimize dyeing conditions of each dye.
Name of variable parameter of dyeing | Parameter varied unit | K/S (Orange CN) | K/S(Green HE4BD) | K/S(Magenta Cold) |
---|---|---|---|---|
Dye concentration (%) | 1 | 7.44 | 11.9 | 2.43 |
2 | 7.69 | 14.98 | 3.6 | |
3 | 9.57 | 15.31 | 5.05 | |
4 | ||||
5 | 9.13 | 15.53 | 7.12 | |
Salt (g/L) | 30 | 9.25 | 8.35 | 3.08 |
40 | 9.68 | 11.37 | ||
50 | 9.73 | 11.97 | 3.55 | |
60 | 3.89 | |||
70 | 7.68 | 11.34 | 3.67 | |
80 | 7.89 | 12.17 | 3.97 | |
Dye exhaustion time (Min.) | 30 | 6.69 | 5.76 | 2.46 |
40 | 7.07 | 5.46 | 2.69 | |
50 | 9.46 | 7.00 | 3.10 | |
60 | 7.43 | |||
70 | 8.00 | 2.99 | ||
80 | 7.79 | 9.44 | 3.17 | |
Dye exhaustion temp (°C) | 60 | 6.20 | 7.35 | — |
70 | 7.65 | — | ||
80 | 7.03 | — | ||
90 | 6.81 | 6.35 | — | |
100 | 5.38 | 5.97 | — | |
Soda Ash(gpl) | 10 | 7.04 | 2.53 | 3.34 |
12 | 7.27 | 3.66 | ||
15 | 7.82 | 4.09 | 4.42 | |
18 | 5.69 | 3.94 | ||
20 | 8.16 | 3.66 | ||
pH | 8 | 6.39 | 4.41 | 3.05 |
9 | 6.42 | 4.96 | 3.22 | |
10 | 6.78 | 5.12 | 3.29 | |
11 | 6.95 | 5.75 | ||
12 | 2.87 | |||
MLR | 1:10 | 10.78 | 6.30 | 3.42 |
1:20 | ||||
1:30 | 10.63 | 4.17 | 3.69 | |
1:40 | 9.68 | 3.38 | 3.09 | |
1:50 | 9.35 | 3.24 | 2.41 |
Surface colour strength (K/S) data showing the effects of dyeing process variables on colour yield of different reactive dyed jute fabric.
Plots of dyeing process variables Vs K/S values for three reactive dyed jute fabric dyed with varying dyeing conditions as per Table 5.
Plots (a-i) showing dyeing process variables vs. K/S curves for three reactive dyes-for varying. (a) Dye concentration; (b) Salt concentration; (c) Dye Exhaustion Time (Min); (d) Dye Exhaustion Temp (oc); (e) Soda Ash (gpl); (f) Dye fixing time (Min); (g) Dye fixing Temp (oc); (h) pH; and (i)MLR.
Finally, data in Table 6 indicate the relevant optimised dyeing parameters for each reactive dyes studied and reported here as optimised dyeing conditions for those respective dyes applied on Jute fabric by conventional reactive dyeing method.
Name of Dye Process Parameters | Re Orange CN | Re Green HE4BD | Re Magenta Cold |
---|---|---|---|
Dye Concentration (%) | 4 | 4 | 4 |
Salt Conc (gpl) | 60 | 60 | 40 |
Dye Exhaustion Time(Min) | 50 | 50 | 40 |
Dye Exhaustion Temp(0C) | 80 | 70 | - |
Soda Ash(gpl) | 18 | 18 | 12 |
Dye Fixing Time(Min) | 45 | 55 | 45 |
Dye Fixing Temp(0C) | 80 | 70 | - |
pH | 12 | 12 | 11 |
MLR | 1:20 | 1:20 | 1:20 |
Optimised conditions of dyeing process variables by conventional method of reactive dyeing of jute fabric using three selected reactive dyes.
In color fastness test for washing, rubbing or crocking or perspiration, or gas fading or any other agencies, the assessment is done two ways—(i) assessing change of colour/loss of depth of shade and (ii) assessing staining on a same or multifiber white fabric after colour fastness test s by fading under different agencies/conditions as per standard test method and followed by assessing colour loss or staining amount by comparing with two types of grey scale as said. But this assessment is sometimes misleading to one grade upper or lower and is debatable unless quantitative measurement of amount of colour change or amount of staining occur is done and checked not fully depending on visual assessment with the said two types of grey scale.
Colour changing grey scale card consists of colour fastness rating for the colour change with a corresponding decreasing scale of grey chroma, which is standardised in 5-grade levels or nine grades system including half grades, where grade 5 representing the best Colour Fastness and grade 1 representing the worst colour fastness. The middle levels are assessed as half grade: like grade 4−5 and grade 3−4 and then it consists of nine levels.
Similarly, stained grey scale card consists of standard scale of white with a corresponding group of increasing grey chroma having standardised mainly by five grades (1–5), or nine grades system including half grades, where grade 5 implies virtually no staining representing best colour fastness while grade 1 signifies the worst colour fastness, and the middle grade are assessed as half grade, like grade 4–5 and grade 3–4. But these grey scale grading is comparative visual assessment of grades and may not always be true.
Hence later, as per ISO-105-A02—1993 Textiles- Test for Color fastness test -part -A02, Grey scale for assessing change in color and ISO-105-A03–2019 - Textiles- Test for Color fastness test- part-A03, Grey scale for assessing staining, the quantitative data for dE* values for both types of grey scale are shown in Table 7 with given tolerances. So precision and correct color fastness grading is now possible matching with the values of measured DE* values after fading/staining on each type of colour fastness tests under different agencies instead of using visual comparative assessment by grey scales only. Thus, colorimetric measurement of these cases is found to be useful for correct/precision color fastness grading.
Grey scale for assessing change in color | ||
---|---|---|
Fastness Grade | CIELAB difference | Tolerance |
5 | 0 | 0.2 |
4.5 | 0.8 | ±0.2 |
4 | 1.7 | ±0.3 |
3–4 | 2.5 | ±0.35 |
3 | 3.4 | ±0.4 |
2–3 | 4.8 | ±0.5 |
2 | 6.8 | ±0.6 |
1–2 | 9.6 | ±0.7 |
1 | 13.6 | ±1.0 |
Fastness grade | CIEIAB difference | Tolerance |
5 | 0 | 0.2 |
4–5 | 2.2 | ±0.3 |
4 | 4.3 | ±0.3 |
3–4 | 6.0 | ±0.4 |
3 | 8.5 | ±0.5 |
2–3 | 12.0 | ±0.7 |
2 | 16.9 | ±1.0 |
1–2 | 24.0 | ±1.5 |
1 | 34.1 | ±2.0 |
Colour fastness grading in terms of colour difference values (dE*) as equivalent to grades of grey scale with tolerances for precision grading of colour fastness assessment.
Rate of dyeing can be understood by colorimetric analysis of dye in fibre (rest are dye in solution) at specific dyeing time and its temperature dependence and dyeing isotherm is understood by Din Fibre vs. Dye in solution plots and dye in fibre with respect to different dyeing temperature indicates its bearing on heat of dyeing. All these can be easily calculated by colorimetric analysis of dye absorbed in fibre (out of total dye added in bath) by analysis of dye concentration left in dyeing bath at any time span and even after different dyeing time and temperature, if dye% added in bath solution before dyeing is known. This must be done in UV VIS absorbance spectrophotometer after obtaining calibrated dye concentrations curve for specific dye. Discussion of a case study will bring more clarity in it to understand it practically. Hence, an example of determining rate of dyeing, dyeing isotherm and dyeing kinetics are briefly mentioned as a case study facilitating both offline and on line colour control in relation to computer aided colour control and matching [21] for textiles.
CASE STUDY 9: An example of determining rate of dyeing, dyeing isotherm [Dye in fibre vs. Dye in Solution curves] and dyeing kinetics (determining half dyeing time, heat of dyeing or dyeing enthalpy, bond energy etc] are briefly mentioned here as case study. Relevant data and the rate of dyeing curve [Df (amount of Dye exhausted to the fibre) vs. td (time of dyeing)] for jute fabric for dyeing with madder (also known as Manjistha/Rubia) after double pre-mordanting with 20% harda (myrobolan) and 20% Al2(S04)3 applied in sequence followed by subsequent dyeing with madder/Manjishtha under a pre-optimized conditions of dyeing are shown in Table 8 and Figure 8.
Time (min) | [D] f, g/kg at 50°C | [D] f, g/kg at 90°C |
---|---|---|
15 | 1.5 | 2.6 |
30 | 2.7 | 4.0 |
45 | 3.8 | 5.2 |
60 | 4.8 | 6.1 |
75 | 5.5 | 6.5 |
90 | 5.9 | 6.6 |
120 | 6.1 | 6.6 |
Dye exhaustion to the fibre [Df] for different dyeing time indicating rate of dyeing for application of Madder/manjistha as natural dye on double pre-mordanted bleached jute fabric.
Rate of dyeing plot as function of time for dyeing of pre-mordanted jute fabric with Madder at 50 and 90°C.
Relevant Data in Table 8 also shows the dye exhaustion to the fibre for different dyeing temperature indicating rate of dyeing for application of madder extract on the said double pre-mordanted jute at lower temperature (at 50°C) and at higher temperature (at 90°C), where differences of dye up take at these two temperature are found to be higher at lower dyeing temperature of dyeing and gradually the differences reduces for use of higher temperature, viz. data in Table 8.
Relevant curves in Figure 8, using data of Table 8, indicate that with increase in dyeing time, the dye uptake (Df) increases measurably up to 60 min of dyeing time and then gradually slows down and almost levels off in between 90 and 120 min. Since, purpurin and manjistin are present as the two main colouring components of in Indian Madder [a natural dye], both these colouring components [having -OH and -COOH functional groups] gradually starts reacting by attachment to mordant with increasing of dyeing time and temperature, while its exhaustion to the mordanted fibre might have levelled off after possible saturation of such dye-mordant-fibre complex forming reaction and possible hydrogen bonding etc. for dye fixation is completed and no further increase in temperature or time can increase dye up take further.
While, Figure 9 is the Plot between Dye in solution (Ds) Vs Dye in Fibre (Df) at a particular time and temperature (here at 90°C) represent at saturation or equilibrium as corresponding dyeing isotherm.
Plot showing the dyeing isotherm for pre-mordanted jute fabric dyed with Madder/Manjistha at 90°C.
The chemical affinity (−∆μ) for the dye molecule or dyeing affinity for Madder/Rubia/Manjistha towards mordants for pre-mordanted bleached jute fabric when dyed at optimized dyeing conditions for different durations at two different dyeing temperatures (50 and 90°C) is shown in Table 5.2.9. Low but measurable increase in chemical affinity of the said colourant is observed for increase in dyeing temperature from 50–90°C, albeit, higher increase in chemical affinity is expected for increase of dyeing temperature. This moderate value and low increase of chemical affinity for enhancement of dyeing temperature showed that dyeing of bleached and mordanted jute fabric with madder/manjistha do not occur as rapidly as expected and maybe there is low extent of formation of Fibre-Mordant-Dye coordinated complex, while it may be presumed that dyeing occur through weak hydrogen bonding formation in a slower speed. While it is reported in earlier literature [22] that some synergistic effects for application of double pre mordanting with 10% natural potash alum and 10% harda (myrobolan) on cotton before dyeing with madder (Manjistha) due to additional coordinating power of chebulinic acid of harda as a mordanting assistant, facilitates more number of strong and giant bigger complex formation amongst the said fibre (cotton)-mordanting assistants (harda)—metallic mordant (natural alum)—natural dye (madder) to develop higher colour strength and higher Colour fastness to wash as an optimised and better option, which however do not happen in case of dyeing jute fabric with madder/manjistha, after double pre-mordanting with 20% harda (myrobolan) and 20% Al2(S04)3 applied in sequence in this case, may be due to acidity of jute do not allow chebulinic acid of harad (myrobolan) to be attracted/absorbed to jute fibre, as required.
To understand the chemistry of attachment of this particular natural colorant specifically whether the dye molecules from madder or manjistha has been bonded to the fibre-mordant system through pre-dominant H-bonding or through coordinate/chelating complex formation, dyeing isotherm indicate that there is formation of more intermolecular H-bonding between dimeric association of – OH groups of madder component and mordanting assistant like harda (myrobolan) used in double mordant attached through metallic mordant of aluminium sulphate and the jute fibre forming intermolecular H-bonds, and less or no Dye-Mordant Fibre Complex formation occur predominantly as expected. Hence the dyeing isotherm observed is Nernst type (and not Langmuir type) is observed in Figure 9 like dyeing of non-polar disperse dyes to hydrophobic polyester fibre. However, some metallic chelate formation cannot be excluded fully and need to be explored by FTIR scan etc.
For dyeing of bleached jute after double pre-mordanting with harda (myrobolan) and Al2(SO4)3, applied in sequence, heat (enthalpy) of dyeing is found to be positive, showing medium magnitudes of positive values. Thus, this dyeing process may be considered as endothermic and therefore more dye would be adsorbed with increase of dyeing temperature up to equilibrium. In case of double pre-mordanting with harda (myrobolan) and Al2(SO4)3 applied in sequence and subsequent dyeing at pH 11.0, K/S value initially increases with increase in dyeing temperature up to 90°C, and above which, the K/S value levelled off. From observed data in Table 9, it is indicated that at dyeing temperature between 50–90°C, the ∆H values (required heat of dyeing, as a measure of bond energy/forces of attraction responsible to bind natural dye molecules to the fibre by bridging through the metallic mordant) are always positive in this case but showing lower magnitude of ∆H values within 6.91 to 29.52 kJ/mol. This bond energy values nearly matches with the usual range of bond energy (10–40 kJ/mol) [23] of hydrogen bond formation indicating formation of a weaker dye-fibre bond that has been taken place instead of coordinated co-valent bonds. The +ve sign of ∆H values might have indicated this dyeing process as an endothermic process, which actually occur for hydrogen bond formation between the dye and mordanted fibre. However, metallic mordanting is also essential to increase the attraction of the dye to the fibre in the dye bath during dyeing to increase their chemical affinity and exhaustion of this natural dye towards jute.
[D]f g/kg | [D]s g/l | —∆μ kJ/mol | ∆H kJ/mol | ∆S J/mol/°K | |||
---|---|---|---|---|---|---|---|
at T1 | at T2 | at T1 | at T2 | at T1 | at T2 | for (T2—T1) | at T2 |
1.5 | 2.6 | 0.031 | 0.016 | 13.31 | 18.62 | 29.52 | 132.62 |
2.7 | 4 | 0.038 | 0.026 | 14.35 | 18.45 | 18.83 | 102.70 |
3.8 | 5.2 | 0.043 | 0.034 | 14.93 | 18.44 | 13.37 | 87.61 |
4.8 | 6.1 | 0.047 | 0.041 | 15.32 | 18.35 | 9.17 | 75.82 |
5.5 | 6.5 | 0.051 | 0.043 | 15.47 | 18.40 | 8.23 | 73.36 |
5.9 | 6.6 | 0.053 | 0.044 | 15.55 | 18.38 | 7.27 | 70.65 |
6.1 | 6.6 | 0.054 | 0.044 | 15.59 | 18.38 | 6.91 | 69.67 |
Thermodynamic parameters for dyeing pre-mordanted jute with Madder/Manjistha after double premordanting with harda plus Aluminium sulphate.
Changes in dyeing entropy (∆S) and dyeing enthalpy (heat of dyeing) are the main indicator of dye absorption and dye fixation force. From observed results in Table 9, it is indicated that for different Df (dye in Fibe) and Ds (dye in solution) values, there is some changes in dyeing entropy at the initial stage of dyeing, with measurable small changes in ∆H values (heat of dyeing), as dyeing time progresses. Df values continues to increase slowly with increase in dyeing time from 30 to 60 min at 90°C in case of said double mordanting system using harda and Al2(SO4)3 in pre-mordanting. This slow increase in K/S value, for increase in dyeing time may be due to only physical absorption of dye molecules in fibre by hydrogen bonding with less possibility of Fibre -Mordant-dye co-ordinated complex formation for the dye fixation even on the pre-mordanted fibre, thus without much affecting ∆H and ∆S values.
For estimation of degree of soiling and soil removal efficiency by standard domestic laundering by selective detergent [24], first the clean white or light coloured fabrics are to be artificially soiled under standard conditions by dipping and running the clean fabric under an oil in water emulsion with water+ coconut oil/carbon tetrachloride with addition of recommended dosages of graphite powder or carbon black powder and the changes in reflectance value after artificial soiling gives degree of soiling as depicted in the following Eq. 25;
Where
Further, estimation of soil removal efficacy % of any detergent, can be similarly calculated by change of Reflectance of corresponding soiled fabric sample before and after washing at specified standard conditions in launder-o-meter, represented by following Eq. 26:
where
The application of above said colorimetric analysis with few case studies for textile industry are a small glimpse only considering this vast subject of colorimetry and hence, this can be applied in makeshift way to other different industry as well. In the colorimetric analysis, besides conventional old model of colorimeter (which is almost abandoned) UV VIS absorbance spectrophotometer and UV VIS Reflectance spectrophotometer, both took major role for colorimetric analyses of all types of Liquid and solid coloured samples used in textile industry, paint industry, food industry, chemical industry, cosmetic industry, pharmaceutical industry etc., where colour information could be obtained with different type of sensor/detector to quantify the colour variation in different colour spaces such as CIE L*a*b* colour space and other recent few more colour space used such as CIE-LUV, RGB, CMC etc., Besides the conventional approaches of colorimetric analysis, non-conventional approaches are now being applied on liquid samples for detection of chlorine in water, to check ripeness estimation of different fruits, to check colour differences in blood to determine blood shading date (or age) for forensic purpose, to determine efficacy of UV active agents like Bluing agents or optical brighteners/UV absorbers used in textile industry etc., where quantification of required colour parameters are calculated using analytical formulas extracted from different colour space concepts defined and measured using UV VIS absorbance spectrophotometer and UV VIS Reflectance spectrophotometer. Presently Portable Reflectance spectrophotometer are the industry’s major choice due to its handy use and carrying capability from one place to other.
As an alternative to UV-VIS spectrophotometric analysis, colorimetry is also widely used in many applications including food allergen testing, albumin testing in urine analysis, blood analysis, pH quantification and water monitoring in different industry.
Over the last decade, scientist has made possible that smartphones may also be used in a variety of scientific fields as spectrometers or as colorimeters, if provided with optical sensor. Smartphone optical spectrometers uses the wavelength scan components, which give spectral information at 400 to 700 nm for the collimated light from the optical source which is dispersed after interaction with samples and corresponding results are recorded. The colour spectrum image of the sample taken in a smart phone is transformed into various colour spaces for the extraction of quantitative colour data. The wavelength of the spectrum generally changes between 400 and 700 nm because of the optical filters set in front of the camera in the manufacturing process which serves the purpose of using this Spectral information in many applications from smart phone.
Smartphone-based spectrometer and colorimetry have been gaining popularity and current relevance due to the widespread advances of these type of small sized and multipurpose smart devices with increasing computational and spectral recording power having relatively low cost and portable designs with very much user-friendly interfaces, and compatibility with data acquisition and processing facility. They find applications in interdisciplinary fields, including but not limited to textiles or paints or pharmaceutical industry, agriculture industry chemical industry and biological and medical purposes too.
IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
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