Calibration models were developed for Fourier Transform Near Infrared Reflectance spectroscopy using PLS method and coefficients of determination (
Around the globe, a 932.2 million hectare area is affected with salinity and sodicity stresses (Metternicht and Zinck,
Salinity affects the growth and development of brassica juncea in various ways. The most common adverse effects of salinity on
In addition to a decrease in the mobilization of photosynthates towards developing siliqua, salinity also adversely affects the deposition of lipids. Fatty acid composition has revealed that erucic acid decreased marginally and this reduction was accompanied by an increase in linolenic acid and eicosenoic acid (Sharma and Manchanda,
The irrigation of mustard with saline water (3500 ppm) raised the values of erucic acid followed by oleic acid to 40.98 and 20.49% respectively, as compared to 30.82 and 15.59% in the controlled treatment (Abd El-Wahab,
Improved genotypes of mustard with tolerance to high salt along with consumer acceptance and good oil quality are required for obtaining optimum yield and expansion of the cultivated area under such stress situation. These concerns prompted an intensive breeding program to develop high yielding cultivars with salinity tolerance at the Central Soil Salinity Research Institute (CSSRI).]
CSSRI has developed three high yielding salt tolerant varieties of Indian mustard (
Indian mustard varieties exhibit quite high contents of erucic acid in oil (more than 40%) (Chauhan
In addition, the high fiber content (12–13%) in the seed meal reflects lower values of metabolizable energy and may negatively influence protein digestibility and the bioavailability of minerals such as magnesium and zinc (Simbaya
In order to utilize or develop genotypes with high contents of oil and protein and low contents of fiber and erucic acid, a rapid and reliable screening of existing genotypes is required.
Plant breeding programs usually involve extensive evaluations of the quality components of interest. Thus, large numbers of screenings by the standard analytical methods of seed lines are usually performed in order to detect target genotypes. Currently, chemical analytical methods are generally used to estimate oil, erucic acid, protein and crude fiber contents. Although the standard analytical techniques usually offer a high level of accuracy and precision, these methods are expensive, time consuming, and require the destruction of seed samples which could be a handicap in the case of valuable and scarce materials.
In recent decades, the development of low cost, non-destructive, high output equipments featuring improved electronic and optical components, the advent of computers capable of effectively processing information contained in spectra and the development of powerful chemo-metric applications has facilitated the expansion of spectroscopic techniques in an increasing number of fields, allowing for an efficient management of spectral and chemical data. The screenings of materials and applications of such selection techniques would increase breeding efficiency.
Newer technological advances have brought about a rapid, lower cost analytical technique called Fourier Transform Near Infrared Reflectance (FT-NIR) spectroscopy. The use of FT-NIR spectroscopy has already been reported for the non-destructive screening of oil, fatty acids, protein, amino acid, and individual and total glucosinolate contents of rapeseed mustard seeds (Petisco
Moreover, FT-NIR spectroscopy is a fast, accurate, and non-destructive technique which requires minimal or no sample preparation and can be used as a replacement of conventional time-consuming chemical methods. It is also interesting to note that NIRS does not require the use of solvents, thus is environmentally friendly, which is currently a major concern. To date, no attempt has been made to assess the impact of salinity on oil quality parameters for mustard on intact seeds by FT-NIR spectroscopy.
Keeping in view the potential advantages of NIRS over chemical methods, the present study was undertaken to develop calibration models and to assess their application in estimating the effects of salinity on oil quality parameters of Indian mustard genotypes and to explore its applicability in identifying variability for these traits.
The experiment was conducted at the Research Farm Karnal (Non-saline field) and out station experimental farm, Nain (Saline field) of the Central Soil Salinity Research Institute (CSSRI), during
All the materials were evaluated in a randomized block design with three replicates in two environments
The plants were grown in 20 kg capacity plastic pots in sand culture and irrigated with five levels of salinity (ECiw 0, 9, 12, 15 and 18 dSm-1) throughout the experiment during 2011–12 and 2012–13. Saline irrigation water was prepared by adding NaCl, CaCl2 and Na2SO4 and maintaining Na: Ca and Cl: SO4 ratio as 4:1 respectively. The pots were arranged in a factorial experiment based on a completely randomized block design (CRBD) with 4 replicates. The seeds were surface-sterilized for 5 minutes in 10% sodium hypochloride solution and then rinsed with distilled water. Five seeds of each genotype were sown at 1 cm depth in each plastic pot (20 Kg capacity) filled with thoroughly washed river sand. The bottom of each pot was delved for drainage of extra water. The pots were irrigated with a nutrient solution (Hoagland’s solution) and maintained at full strength field capacity till germination. After germination, the number of plants was reduced to two seedlings per pot. Salinity stress was imposed at the four leaf stage and different levels of salinity were achieved by step-wise addition of saline solution to each pot so as to avoid shock. Thereafter, the salinity levels were maintained throughout the experiment by flushing the salt daily from the pots. At harvest seed yield was recorded. The whole seeds obtained were subjected to the above mentioned analysis using FT-NIR.
Sixty-nine seed samples of mustard genotypes were obtained from the Indian Agricultural Research Institute, New Delhi, Directorate of Rapeseed and Mustard Research, Bharatpur and Punjab Agricultural University, Ludhiana, India during 2012–13. These samples were pre-analyzed in the laboratory for oil, protein, erucic acid and crude fiber contents with chemical methods. The oil content (%) was estimated using nuclear magnetic resonance (NMR), according to the protocol of the AOCS (
All the samples were representing the spectral and chemical variability in the mustard in the calibration and validation groups used for preparation of the library and standardization of FT-NIR.
Seed samples were analyzed as intact and NIR spectra were recorded in reflectance mode using an FT-NIR spectrometer (Perkin Elmer, Massachusetts, USA) equipped with an integrative sphere, over the range of 10000 - 4000 cm-1 at 1 nm interval. The seeds were then stored. The spectrum of each sample was the average of 32 scans. Spectrum10 software (Perkin Elmer, Massachusetts, USA) was used for spectral acquisition and instrumental control.
Data pre-treatment using mathematical transformations (e.g. derivatives, multiple scatter correction, smoothing) of the NIR spectra was applied to enhance spectral features and/or remove or reduce unwanted sources of variation. The spectral data sets were correlated with oil, protein, erucic acid and crude fiber content using partial least squares (PLS) regression algorithm. Calibrations were performed using Spectrum Quant+ software (v.4 60). To evaluate the calibration performance of the developed models, a cross validation was used and also a test set validation was performed using coefficients of determination (
The mean, standard deviation and coefficient of variability for different characters among quality characters were worked out following SAS 9.2 software.
During the process of development of the calibration model and its validation, a certain number of samples was excluded in order to obtain the most reliable model possible. Therefore, the number of samples used for developing calibration (
Calibration and validation statics in FT-NIR models for the estimation of oil, protein, erucic acid and crude fibers content in mustard
Oil% | Protein% | Erucic acid% | Crude fiber% | |||||
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Statistics | Calibration | Validation | Calibration | Validation | Calibration | Validation | Calibration | Validation |
No. Sample | 69 | 32 | 69 | 32 | 69 | 32 | 69 | 32 |
Mean | 39.24 | 39.20 | 19.45 | 19.30 | 39.61 | 39.10 | 10.91 | 10.50 |
Range | 37.0–41.5 | 37.1–41.3 | 17.6–20.2 | 17.0–20.0 | 0.0–57.30 | 0.02–57.0 | 6.3–16.7 | 6.1–17.1 |
SD | 4.720 | 4.750 | 3.200 | 3.100 | 3.720 | 3.720 | 2.040 | 2.100 |
CV | 12.029 | 12.117 | 16.452 | 16.062 | 9.392 | 9.514 | 18.698 | 20.000 |
|
0.900 | 0.907 (Y = 0.290x+ 27.82) | 0.910 | 0.922 (Y = 0.607x+ 7.711) | 0.910 | 0.902 (Y = 0.869x+ 3.402) | 0.910 | 0.903 (Y = 0.235x+ 7.835) |
SEE | 0.70 | – | 0.50 | – | 0.73 | – | 0.36 | – |
SEP | – | 1.01 | – | 0.68 | – | 0.80 | – | 0.43 |
RPD | 4.67 | 4.71 | 4.65 | 4.74 |
SD: Standard deviation, CV: Coefficient of variation,
The results of the statistics related to the PLS calibration model using full cross validation obtained by FT-NIR technology for the oil, protein, erucic acid and crude fiber contents are shown in (
The equations developed for
The analysis of variance for both field as well as pot study showed significant mean squares of genotypes for oil, protein erucic acid and crude fiber content, indicating significant differences among the genotypes. Significant mean squares of salinity for all studied traits indicated differences among salinity levels and its influence on these traits. The interaction effects of salinity levels and genotypes were significant for all the traits, indicating the different trend of variations among the genotypes at different salinity levels (
Pooled ANOVA of experiments conducted during 2011–12 and 2012–13
df | Mean Sum of Square | ||||
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Source | Variable | Pot | Field | Pot | Field |
Replication | Oil% | 3 | 2 | 0.05 | 0.72 |
Protein% | 0.12 | 1.31 | |||
Erucic acid% | 0.01 | 11.95 | |||
Crude fiber% | 0.01 | 2.24 | |||
Genotype | Oil% | 1 | 96 | 1.06 |
0.40 |
Protein% | 0.58 |
0.82 |
|||
Erucic acid% | 1.06 |
38.09 |
|||
Crude fiber% | 0.82 |
5.01 |
|||
Salinity | Oil% | 4 | 1 | 13.44 |
2434.05 |
Protein% | 86.59 |
2374.95 |
|||
Erucic acid% | 1021.12 |
5923.16 |
|||
Crude fiber% | 32.81 |
1650.39 |
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Year | Oil% | 1 | 1 | 0.02 | 0.10 |
Protein% | 0.05 | 0.19 | |||
Erucic acid% | 0.90 | 2.73 | |||
Crude fiber% | 0.01 | 0.46 | |||
Genotype |
Oil% | 4 | 96 | 4.15 |
12.57 |
Protein% | 2.33 |
6.94 |
|||
Erucic acid% | 4.23 |
28.94 |
|||
Crude fiber% | 3.65 |
45.56 |
|||
Genotype |
Oil% | 1 | 96 | 0.94 | 0.13 |
Protein% | 0.01 | 0.31 | |||
Erucic acid% | 0.09 | 2.73 | |||
Crude fiber% | 0.02 | 0.46 | |||
Salinity |
Oil% | 4 | 1 | 0.40 | 5.03 |
Protein% | 0.61 | 3.93 | |||
Erucic acid% | 0.14 | 17.48 | |||
Crude fiber% | 0.03 | 0.01 | |||
Genotype |
Oil% | 4 | 96 | 0.63 |
8.25 |
Protein% | 0.36 |
4.76 |
|||
Erucic acid% | 0.62 |
20.55 |
|||
Crude fiber% | 0.56 |
32.57 |
|||
Error | Oil% | 60 | 776 | 0.25 | 0.30 |
Protein% | 0.13 | 0.52 | |||
Erucic acid% | 0.24 | 4.35 | |||
Crude fiber% | 0.21 | 1.00 |
Significant at 5% and 1% levels respectively.
The mean seed oil content showed a range of 36.92–39.81% and 36.13–38.34% in the field experimentats and control conditions (pots), respectively and was less variable as seen by the relatively lower values for coefficients of variability (CV= 1.40% in field and 0.70% in pot). With the increase in salinity to ECe 10.7 dSm-1 under field conditions, the seed oil content decreased by 7.27%, whereas, the oil content decreased by 5.78% at ECe 18 dSm-1 under control conditions (
Summary of the effects of salinity on quality parameters of mustard
Variate | Range over the salinity | Mean over year and salinity | % decrease or increase over control | |||||
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SD | CV% | 9 dS·m-1 | 12 dS·m-1 | 15 dS·m-1 | 18 dS·m-1 | |||
Oil% | 36.13–38.34 | 37.62 | 0.27 | 0.70 | 0.15 | –1.82 | –2.02 | –5.78 |
Protein% | 13.63–19.29 | 17.19 | 0.36 | 2.10 | –2.57 | –6.06 | –16.46 | –29.31 |
Erucic acid% | 27.04–46.62 | 34.46 | 0.21 | 0.60 | 6.08 | 18.14 | 40.59 | 72.43 |
Crude fiber% | 6.00–9.13 | 8.03 | 0.07 | 0.90 | –0.16 | –2.55 | –22.81 | –34.25 |
Oil% | 36.92–39.81 | 38.37 | 0.54 | 1.40 | –7.27 | |||
Protein% | 16.50–19.35 | 17.92 | 0.72 | 4.00 | –14.78 | |||
Erucic acid% | 36.93–41.47 | 39.22 | 2.08 | 5.30 | 12.20 | |||
Crude fiber% | 9.26–11.64 | 10.45 | 0.98 | 9.40 | –20.45 |
The protein content was more variable, as seen by relatively high coefficients of variability (CV = 2.10%) in pot condition and less variable in field condition (CV = 4.0%) than the crude fiber content (CV = 0.90% in pot and 9.40% in field). Protein and crude fiber contents decreased by 29.31% and 34.25% in pots and 14.78% and 20.45% in field conditions, respectively, at high salinity levels (
The mean erucic acid among the varieties did not vary over the cropping seasons in the pot experiment (CV = 0.60%) but in the field study, it was more variable than oil and protein contents (CV = 5.30%). It ranged from 27.04%–46.62% and 36.93%–41.47% in pot and field experiments, respectively. The erucic acid content increased by 72.43% in the pots at ECiw 18 dSm-1 compared to the non-saline control; whereas, it increased by 12.20% in the field at a high salinity level of ECe 10.70 dSm-1 (
The present investigation revealed that FT-NIR is an accurate and powerful technique that could be applied successfully for rapid mass screening of the potential germ plasm for selecting high oil and protein, low crude fiber and erucic acid contents. The threshold limit of salinity for mustard, up to which little or no reduction in yield occur, is 8.2 dSm-1 under soil (ECe) and 12 dSm-1 for irrigation water (ECiw), and for every increase of 1 dSm-1 in EC above the salinity threshold results in a reduction in the oil, protein and crude fiber of about 1–3%, 4–6% and 5–8%, respectively, whereas erucic acid increased by 5–9%.
Authors express sincere thanks to Director, CSSRI, Karnal for encouragement and to Director, DRMR, Bharatpur, Dr. D.K. Yadav, Division of Genetics, IARI, New Delhi and Head, Division of Genetics and Plant Breeding, PAU Ludhiana, India for providing the seed samples of