Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Abstract

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from ‘Farm’ to ‘Table’: Progresses, challenges, trends, and prospects

“…PCR can be expressed as Y = c 0 + c 1 PC 1 + c 2 PC 2 + ∙∙∙ + c k PC k + ε” ( Y is response variable; c 0 is intercept; c 1 ,∙∙∙, c k are coefficients; PC 1 , ∙∙∙, PC k are retained PCs, ε” is error term). MPLS, SOPLS, LWPLS and RBF-PLS are extensions or derivative versions of traditional PLS algorithm to enhance the capabilities in dealing with complex datasets ( Font, del Río-Celestino, Luna, Gil, & de Haro-Bailón, 2021 ; Lesnoff, Metz, & Roger, 2020 ).…”

Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy

“…Recently, the use of NIRS models for predicting the quality of vegetables has been reported, several of which have addressed zucchini [19,20], pepper, rocket leaves, blackberries [16,21,22] and Ethiopian mustard leaves [23], among others. The seed quality of various legume species has also been analyzed using NIRS such as lentils [25], chickpeas [26] and pea accessions from different germplasm collections [27,28]. Other studies have focused on predicting the sensory quality and maturity of peas [29,30] using NIRS.…”

Non‐destructive determination of grass pea and pea flour adulteration in chickpea flour using near‐infrared reflectance spectroscopy and chemometrics

“…Although a number of methods for adulteration detection are available, no study has been reported for detection and quantification of grass pea and pea flour adulterants in chickpea flour using NIRS by coupling with chemometrics. The application of NIRS in context of chick pea flour mainly includes determination of chemical constituents of chick pea flour, 27 detection of neutral and acid detergent fiber 28 . We have recently reported detection of maize flour adulteration in chickpea flour using NIRS 29 .…”

“…The application of NIRS in context of chick pea flour mainly includes determination of chemical constituents of chick pea flour, 27 detection of neutral and acid detergent fiber. 28 We have recently reported detection of maize flour adulteration in chickpea flour using NIRS. 29 As the development and validation of screening method for individual parameter and commodity is a prerequisite, 30 we therefore, for the first time, report NIRS methods for the detection of two legume flours, namely grass pea and pea, adulterants in chickpea flour.…”