NIR hyperspectral imaging with multivariate analysis for measurement of oil and protein contents in peanut varieties

The potential of hyperspectral imaging in the spectral range of 1000–2500 nm with multivariate analysis for the prediction of oil and protein concentrations in five peanut cultivars was investigated. Quantitative partial least squares regression (PLSR) models were established using the extracted spectral data from hyperspectral images and the reference measured oil and protein concentrations. The PLSR models established using the whole spectral data pretreated by the multiplicative scatter correction (MSC) method showed good results for predicting the oil concentration with a determination coefficient (RP2) of 0.945 and root mean square errors of prediction (RMSEP) of 0.196 and for predicting the protein concentration with RP2 of 0.901 and RMSEP of 0.441. In addition, eight optimal wavelengths were selected for protein and oil contents, respectively, using the regression coefficients of the PLSR analysis and used for simplifying the obtained models. The simplified PLSR models also showed good performances with Rp2 of 0.933 and 0.912 for predicting oil and protein concentrations. The whole results demonstrated that the NIR hyperspectral imaging technique coupled with chemometric analysis is a promising tool for rapid and non-destructive determination of oil and protein concentrations in peanut kernels and has the potential to develop a multispectral imaging system for future on-line detection of peanut quality.

[1]  S. Chatterjee,et al.  Regression Analysis by Example , 1979 .

[2]  Da-Wen Sun,et al.  Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: Current research and potential applications , 2014 .

[3]  T. Brenneman,et al.  Evidence for a Second RKN Resistance Gene in Peanut , 2016 .

[4]  Hanping Mao,et al.  Classification of Black Beans Using Visible and Near Infrared Hyperspectral Imaging , 2016 .

[5]  Christopher L. Butts,et al.  Application of near infrared spectroscopy to peanut grading and quality analysis: overview , 2009 .

[6]  H. Byrne,et al.  Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells. , 2016, Chemical Society reviews.

[7]  Paul J. Williams,et al.  Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis. , 2009, Analytica chimica acta.

[8]  Graeme C. Wright,et al.  Feasibility of estimating peanut essential minerals by near infrared reflectance spectroscopy , 2011 .

[9]  Paul J. Williams,et al.  Classification of maize kernels using NIR hyperspectral imaging. , 2016, Food chemistry.

[10]  Noel D.G. White,et al.  Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging , 2016 .

[11]  Chari V. Kandala,et al.  NIR Reflectance Spectroscopy for Nondestructive Moisture Content Determination in Peanut Kernels , 2009 .

[12]  Paul J. Williams,et al.  Near infrared hyperspectral imaging in quality and safety evaluation of cereals , 2018, Critical reviews in food science and nutrition.

[13]  Vincent Baeten,et al.  Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review , 2013 .

[14]  H. Büning-Pfaue.  Analysis of water in food by near infrared spectroscopy , 2003 .

[15]  H. Suleria,et al.  Physicochemical Characteristics, Functional Properties, and Nutritional Benefits of Peanut Oil: A Review , 2014, Critical reviews in food science and nutrition.

[16]  Da-Wen Sun,et al.  Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. , 2017, Meat science.

[17]  Noel D.G. White,et al.  Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products , 2016, Food and Bioprocess Technology.

[18]  Noel D.G. White,et al.  Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat , 2014, Food and Bioprocess Technology.

[19]  Da-Wen Sun,et al.  Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging , 2014 .

[20]  William R. Windham,et al.  Determination of In-Shell Peanut Oil and Fatty Acid Composition Using Near-Infrared Reflectance Spectroscopy , 2010 .

[21]  Tzu-Tsung Wong,et al.  Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation , 2015, Pattern Recognit..