Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue

Electronic Tongues and Noses: A General Overview

As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

Assessment of Maturity of Plum Samples Using Fourier Transform Near-Infrared Technique Combined with Chemometric Methods

The FT-NIR technique was used for rapid and non-destructive determination of plum ripeness. The dry matter (DM), titratable acidity (TA), total soluble solids (TSS) and calculated maturity index (MI: TSS/TA) were used as reference values. The PLS correlations were validated via five-fold cross-validation (RMSECV for different parameters: DM: 0.66%, w/w; TA = 0.07%, w/w; TSS = 0.72%, w/w; MI = 1.39) and test set validation (RMSEP for different parameters: DM: 0.65%, w/w TA = 0.07%, w/w; TSS = 0.61%, w/w; MI = 1.50). Different classification algorithms were performed for TA, TSS and MI. Linear, quadratic and Mahalanobis discriminant analysis (LDA, QDA, MDA) were found to be the best sample detection methods. The accuracy of the classification methods was 100% for all investigated parameters and cultivars.

A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

Introduction

Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures.

Methods

This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison.

Results

The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively.

Discussion

These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.

Juice Quality Evaluation with Multisensor Systems-A Review

E-nose and e-tongue are advanced technologies that allow for the fast and precise analysis of smells and flavours using special sensors. Both technologies are widely used, especially in the food industry, where they are implemented, e.g., for identifying ingredients and product quality, detecting contamination, and assessing their stability and shelf life. Therefore, the aim of this article is to provide a comprehensive review of the application of e-nose and e-tongue in various industries, focusing in particular on the use of these technologies in the fruit and vegetable juice industry. For this purpose, an analysis of research carried out worldwide over the last five years, concerning the possibility of using the considered multisensory systems to test the quality and taste and aroma profiles of juices is included. In addition, the review contains a brief characterization of these innovative devices through information such as their origin, mode of operation, types, advantages and disadvantages, challenges and perspectives, as well as the possibility of their applications in other industries besides the juice industry.

Application of Near Infrared Spectroscopy to Monitor the Quality Change of Sour Cherry Stored under Modified Atmosphere Conditions

Determining and applying ‘good’ postharvest and quality control practices for otherwise highly sensitive fruits, such as sour cherry, is critical, as they serve as excellent media for a wide variety of microbial contaminants. The objective of this research was to report two series of experiments on the modified atmosphere storage (MAP) of sour cherries (Prunus cerasus L. var. Kántorjánosi, Újfehértói fürtös). Firstly, the significant effect of different washing pre-treatments on various quality indices was examined (i.e., headspace gas composition, weight loss, decay rate, color, firmness, soluble solid content, total plate count) in MAP-packed fruits. Subsequently, the applicability of near infrared (NIR) spectroscopy combined with chemometrics was investigated to detect the effect of various storage conditions (packed as control or MAP, stored at 3 or 5 °C) on sour cherries of different perceived ripeness. Significant differences were found for oxygen concentration when two perforations were applied on the packages of ‘Kántorjánosi’ (p < 0.01); weight loss when ‘Kánorjánosi’ (p < 0.001) and ‘Újfehértói fürtös’ (p < 0.01) were packed in MAP; SSC when ‘Újfehértói fürtös’ samples were ozone-treated (p < 0.05); and total plate count when ‘Kántorjánosi’ samples were ozone-treated (p < 0.01). The difference spectra reflected the high variability in the samples, and the detectable effects of different packaging. Based on the investigations with the soft independent modelling of class analogies (SIMCA), different packaging and storage resulted in significant differences in most of the cases even on the first storage day, which in many cases increased by the end of storage. The soft independent modelling of class analogies proved to be suitable for classification with apparent error rates between 0 and 0.5 during prediction regardless of ripeness. The research findings suggest the further correlation of NIR spectroscopic and reference parameters to support postharvest handling and fast quality control.

Comparison of the Fungal Community, Chemical Composition, Antioxidant Activity, and Taste Characteristics of Fu Brick Tea in Different Regions of China

In this study, the fungal community structure, metabolites, antioxidant ability, and taste characteristics of five Fu brick tea (FBT) from different regions of China were determined and compared. A total of 69 operational taxonomic units (OTUs) were identified and assigned into 5 phyla and 27 genera, with Eurotium as the predominant genus in all samples. Hunan (HN) sample had the strongest fungal diversity and richness, followed by Guangxi (GX) sample, and Zhejiang (ZJ) sample had the lowest. GX sample had higher amounts of gallic acid (GA), total catechins, gallocatechin (GC), and epicatechin gallate (ECG) as well as antioxidant activity than the other samples. The levels of total phenolics, total flavonoids, epigallocatechin (EGC), catechin, epicatechin (EC), thearubigins (TRs), and theaflavins (TFs) were the highest in the ZJ sample. Guizhou (GZ) and Shaanxi (SX) samples contained the highest contents of epigallocatechin gallate (EGCG) and gallocatechin gallate (GCG), respectively. Total phenolics, GA, EC, CG, and TFs were positively associated with most of fungal genera. Total phenolic content (TPC), total flavonoid content (TFC), and most of catechins contributed to the antioxidant activities of FBT. HN sample had the strongest sourness and sweetness, ZJ sample had the strongest saltiness, SX sample had the strongest umami, and GZ sample had the strongest astringency, which was ascribed to the varied metabolites. This work reveals that FBT in different regions vary greatly in fungal community, metabolites, antioxidant activity, and taste characteristics, and provides new insight into the quality characteristics formation of FBT in different regions.