Discrimination of Chinese traditional soy sauces based on their physico-chemical properties

Abstract

This work aimed to classify the categories (produced by different processes) and brands (obtained from different geographical origins) of Chinese soy sauces. Nine variables of physico-chemical properties (density, pH, dry matter, ashes, electric conductivity, amino nitrogen, salt, viscosity and total acidity) of 53 soy sauce samples were measured. The measured data was submitted to such pattern recognition as cluster analysis (CA), principal component analysis (PCA), discrimination partial least squares (DPLS), linear discrimination analysis (LDA) and K-nearest neighbor (KNN) to evaluate the data patterns and the possibility of differentiating Chinese soy sauces between different categories and brands. Two clusters corresponding to the two categories were obtained, and each cluster was divided into three subsets corresponding to three brands by the CA method. The variables for LDA and KNN were selected by the Fisher F-ratio approach. The prediction ability of all classifiers was evaluated by cross-validation. For the three supervised discrimination analyses, LDA and KNN gave 100% predications according to the sample category and brand.

Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

“…Generally, LDA, KNN and PLSDA as the most commonly used linear discrimination methods, have presented their great potential in qualitative analysis2122, thus they were chosen to apply in this study and all of them achieved good performances except for PLSDA model; this could be interpreted as follows: algorithms of LDA and KNN were used as soft classifiers where it is desired to estimate the probability that the correct identification is category, but PLSDA was used as hard classifier where the probabilities were not of primary interest, for instance, in cases where the object is easily classifiable by a human23. However, the correlation between the NIR laser scattering images and properties of bacterial colonies could incline to nonlinearity, whereas linear discrimination tools may not provide a complete solution to such nonlinear case in this work; moreover, nonlinear approaches are stronger than linear approaches in terms of self-learning and self-adjust22.…”

Study on Classification of Soy Sauce by Electronic Tongue Technique Combined with Artificial Neural Network

“…In the past few years, soy sauce quality has been assessed by many different analytical tools, for example, capillary zone electrophoresis with amperometric detection (Chu and others 2010), capillary gas chromatography with mass spectrometric (Chung and others 2002), capillary electrophoresis with electrochemical detection (Xing and Cao 2007), liquid chromatography coupled with mass spectrometry (Sano and others 2007), and high‐performance liquid chromatography (Stute and others 2002). In 2010, Chen and others attempted to classify brands and categories of soy sauce based on their physico‐chemical properties, linear discrimination analysis, and K‐nearest neighbor, giving 100% predication according to the category and brand of the samples. All of these analysis methodologies mentioned above show good precision, accuracy, and reliability; however, they are destructive, time consuming, and also require expensive equipment.…”