Morphological, physiochemical and colour characteristics of fresh and cured starch in potato varieties

The present study was conducted to study the morphological, physicochemical and colour characteristics of potato starch extracted by control and combined methods from potato varieties viz., Kufri Chipsona-4, Badshah, Pushkar, Bahar and Sindhuri (fresh and cured). Among these varieties, Kufri Chipsona-4 exhibited maximum percent of small size (< 30 μm) particles (48%). Kufri Sindhuri showed highest starch purity (87.1%) but lowest whiteness (92.2%) whereas, highest whiteness (95.4%) was recorded in starch extracted from Kufri Badshah. Among starch extraction methods, combined method showed significantly lower starch moisture content (11.8%), fat (0.28%), protein (0.31%), ash (0.28%) and crude fibre (0.15%) whereas; starch purity (87.2%), percentage of small size particles (45%) and starch whiteness (96.3%) were observed higher than control methods in all varieties

Fishy forensics: FT-NIR and machine learning based authentication of Mediterranean anchovies (Engraulis encrasicolus)

Seafood authentication and traceability is a challenging issue owing to its complex supply chain and fishing in international waters. NIR spectroscopy has been successfully used to authenticate food of animal and plant origin. In this study, FT-NIR was used to discriminate between Mediterranean anchovies (Engraulis encrasicolus) fished from Adriatic, Balearic, and Tyrrhenian Sea. The spectra were prepared using the standard normal variate (SNV) and the Savitzky-Golay 1st derivative, 2nd order (SG), as well as both together. The model was built, after outlier removal, with linear-support vector machine (L-SVM), polynomial-SVM (P-SVM), k-nearest neighbor (k-NN) and Random Forest (RF). Spectral preprocessing improved model classification accuracy for all algorithms. The data could not be put into groups by linear algorithms like L-SVM and k-NN because the NIR spectra were not linear and had many columns. Non-linear algorithms, P-SVM and RF when coupled with SG+SNV, successfully produced models with maximum robustness. P-SVM and RF models had 100 % accuracy in training set and 95.7 % and 95.5 % accuracy in testing set, respectively and 95.2 and 95.1 accuracy in cross-validation set

What the fish? Tracing the geographical origin of fish using NIR spectroscopy

Food authentication is a growing concern with rising complexities of the food supply network, with fish being an easy target of food fraud. In this regard, NIR spectroscopy has been used as an efficient tool for food authentication. This article reviews the latest research advances on NIR based fish authentication. The process from sampling/sample preparation to data analysis has been covered. Special attention was given to NIR spectra pre-processing and its unsupervised and supervised analysis. Sampling is an important aspect of traceability study and samples chosen ought to be a true representative of the population. NIR spectra acquired is often laden with overlapping bands, scattering and highly multicollinear. It needs adequate pre-processing to remove all undesirable features. The pre-processing technique can make or break a model and thus need a trial-and-error approach to find the best fit. As for spectral analysis and modelling, multicollinear nature of NIR spectra demands unsupervised analysis (PCA) to compact the features before application of supervised multivariate techniques such as LDA, PLS-DA, QDA etc. Machine learning approach of modelling has shown promising result in food authentication modelling and negates the need for unsupervised analysis before modelling