Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

Kamboj, Uma; Guha, Paramita; Mishra, Sunita

doi:10.1080/00032719.2016.1247163

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…Although NIRS has been used for calibration purposes in a wide range of food products, legumes have attracted less attention in general (Lee and Choung, 2009;Pojić et al, 2010;Szigedi et al, 2013;Kamboj et al, 2017). Regarding lentils, the NIRS technique has been used only to determine protein (Lazzeri et al, 1990) and color (Black and Panozzo, 2004).…”

Section: Introductionmentioning

confidence: 99%

Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy

Revilla

Lastras

González-Martín

et al. 2019

Journal of Food Composition and Analysis

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Section: Introductionmentioning

confidence: 99%

Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy

Revilla

Lastras

González-Martín

et al. 2019

Journal of Food Composition and Analysis

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“…Absorption spectroscopy is largely used to determine the quality attributes of food. Also, it gives broad picture regarding the physical and chemical nature of foods; for example, Kamboj et al [47] quantified components of the chickpea flour by using Near-Infrared Spectrometer. The technique involves the differences between the spectral lines/ fingerprints of fresh and aged food samples.…”

Section: Near-infrared Spectroscopy: Spectra Measurementsmentioning

confidence: 99%

Influence of Yeast β-Glucan on Cookies Sensory Characteristics and Bioactivities

Bacha

Nasir

Iqbal

et al. 2018

Journal of Chemistry

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β-Glucan is biologically active polysaccharide, ubiquitously found in many grains, bacteria, and fungi and much yeast. The aim of this study was to determine the effect of substituting wheat flour by 1, 2, and 4% yeast isolated β-glucan in cookies on the sensory acceptance, antioxidants, oxidative stability, and quality evaluation which were investigated. According to the results, cookies supplemented at 2% yeast β-glucan were proved satisfactory on sensory quality perspective. During the storage study it was found that cookies made with 2 and 4% β-glucan have effectively (p>0.05) kept the peroxide value (PV) within acceptable range, demonstrating the promising role of β-glucan in deterring oxidative. It is further noted that 2 or 4% β-glucan incorporated cookies assimilated highest absorption spectra, suggesting the retardation in freshness losses, with having minimum microbial loads, showing microbiological safety. β-Glucan fortification in foods is technologically and economically feasible, suggesting that a significant prospect of β-glucan as low-cost food ingredient in formulating cookies at 2% offers exciting new use of β-glucan of yeast origin.

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“…Chickpea is the third-largest produced pulse in the world, with 11.6 million tonnes produced per annum, 80% of which is desi, and the remaining 20% is kabuli (Merga and Haj i, 2019). Near-infrared (NIR) spectroscopy is used to rapidly characterize quality parameters in desi chickpea flour using Partial Least Square Regression (PLSR) to determine protein, carbohydrate, fat and moisture concentrations of chickpea (Kamboj et al, 2016). It is used to quantify the reflection rates of infrared light radiation within a sample, which is then used to estimate the chemical contents of the sample using machine learning modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

Comparing Various Machine Learning Algorithms for Sugar Prediction in Chickpea using Near-infrared Spectroscopy

Priyadarshi¹,

Sharma²,

Chaturvedi³

et al. 2022

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Background: Chickpea is the third major pulse produced globally, with 11.6 million tonnes produced per annum (Merga and Haji, 2019). Sugar alcohols, inulin, starch are all prebiotic carbohydrates found in chickpeas (Johnson et al., 2020). Near-Infrared (NIR) spectroscopy is a non-destructive, versatile and powerful analytical technique. Methods: Spectral data obtained from NIR spectroscopy requires application of various techniques to extract useful information from spectral data which is further used for building various models for prediction of physical or chemical components presents in agricultural crops. The main aim of this study is to apply various machine learning algorithms especially effective in predicting sugar concentration in chickpea. Sugar prediction models are developed using Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR) and Decision Tree Regression (DTR) algorithms. Performance of the models is evaluated using measures namely, Root Mean Square Error (RMSE), Residual Standard Error (RSE), Coefficient of Determination (R2) and Adjusted Coefficient of Determination (adjusted R2). Result: It was observed that, RF outperformed all other models in terms of accuracy for predicting sugar component from preprocessed spectra, with RMSE, RSE, R2 and adjusted R2 values of 0.054, 0.062, 0.954 and 0.937, respectively. The accuracy of the ANN model is similar to that of the RF, with minor differences in RMSE, RSE, R2 and adjusted R2 , values of 0.057, 0.067, 0.952 and 0.935.

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Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

Cited by 18 publications

References 19 publications

Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy

Predicting the physicochemical properties and geographical ORIGIN of lentils using near infrared spectroscopy

Influence of Yeast β-Glucan on Cookies Sensory Characteristics and Bioactivities

Comparing Various Machine Learning Algorithms for Sugar Prediction in Chickpea using Near-infrared Spectroscopy

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