Prediction of Main Potato Compounds by NIRS
Lopez-Maestresalas, A.
Perez, C.
Tierno, R.
Arazuri, S.
Ruiz De Galarreta, J.I.
Jaren, C.
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How to Cite

Lopez-Maestresalas A., Perez C., Tierno R., Arazuri S., Ruiz De Galarreta J., Jaren C., 2017, Prediction of Main Potato Compounds by NIRS , Chemical Engineering Transactions, 58, 385-390.
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Potato (Solanum tuberosum, L) compounds are generally determined by analytical methods including gas- liquid chromatography (GLC), HPLC and UV-VIS spectrophotometry. These methods require a lot of time and are destructive. Therefore, they seem to be not suitable for in-line applications in the food industry. Near- infrared spectroscopy (NIRS) is a technique that presents some advantages over reference methods for quantitative analysis of agricultural and food products since it is fast, reliable and non-destructive.
For this reason, in this study, quantitative analyses were carried out to determine main compounds in potatoes using NIRS.
Potato tubers grown in two consecutive years were used for the analyses. NIR spectral acquisition was acquired on lyophilized samples. In year 1, a total of 135 samples were used while 228 samples were used in year 2. Lyophilized samples were also scanned by NIRS, two replicates per samples were acquired and the mean spectrum of each sample was used for the analysis.
Different chemical analyses were carried out each year. Thus, in year 1 the following parameters were quantified: reducing sugars (RS) and nitrogen (N), whereas in year 2, total soluble phenolics (TSP) and hydrophilic antioxidant capacity (HAC) were extracted and quantified. Then, chemometric analyses were performed using Unscrambler X (version 10.3, CAMO software AS, Oslo, Norway) to correlate wet chemical analysis with spectral data. Quantitative analyses based on PLS regression models were developed in order to predict the above chemical compounds of tubers in a non-destructive manner.
Good PLS regression models were obtained for the prediction of nitrogen and TSP with coefficients of determination (R2) above 0.83. Moreover, PLS models obtained for the estimation of HAC could be used for screening and approximate calibrations.
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