Contemporary Developments and Emerging Trends in the Application of Spectroscopy Techniques: A Particular Reference to Coconut (Cocos nucifera L.)

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners

Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models. The dataset comprises infrared spectra from coconut water samples spiked with 15 different types of potential sugar substitutes, including: sugars, artificial sweeteners, and sugar alcohols. The interaction of infrared light with molecular bonds generates unique molecular fingerprints, forming the basis of our analysis. Departing from previous research predominantly reliant on linear-based chemometrics for adulterant detection, our study explored linear, non-linear, and combined feature extraction models. By developing an interactive application utilizing principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), non-targeted sugar adulterant detection was streamlined through enhanced visualization and pattern recognition. Targeted analysis using ensemble learning random forest (RF) and deep learning 1-dimensional convolutional neural network (1D CNN) achieved higher classification accuracies (95% and 96%, respectively) compared to sparse partial least squares discriminant analysis (sPLS-DA) at 77% and support vector machine (SVM) at 88% on the same dataset. The CNN's demonstrated classification accuracy is complemented by exceptional efficiency through its ability to train and test on raw data.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

A comparison of the quality of plain yogurt and its analog made from coconut flesh extract

The aim of this study was to compare the quality of plain yogurt made from cow milk (n = 10) and its plant-based analog made from coconut flesh extract (n = 14). Coconut yogurt alternatives were divided into 2 experimental groups based on differences in their color, which were noted after the packages had been opened. The first group included products with a typical white color (n = 8), and the second group comprised products with a grayish pink color (n = 6) that developed as a result of oxidative processes. In comparison with its plant-based analog, plain yogurt was characterized by higher values of lightness (L*), yellowness (b*) and chroma (C*), higher titratable acidity, a higher content of retinol and α-tocopherol, higher nutritional value of fat, and lower values of water-holding capacity (WHC) and redness (a*). Plain yogurt had lower volatile acidity than its plant-based analog with a grayish pink color. A comparison of yogurt analogs with different colors revealed that the product with a grayish pink color was characterized by a lower value of L*, and higher values of a*, b*, C*, and pH. An analysis of its fatty acid profile demonstrated that it also had a higher proportion of C14:0 and C18:1 cis-9; higher total monounsaturated fatty acids content; a lower proportion of C10:0, C12:0, and C18:2; a lower total content of polyunsaturated fatty acids (PUFA) and essential fatty acids; and a lower ratio of PUFA to saturated fatty acids. The yogurt analog with a grayish pink color had a lower total content of tocopherol isoforms than the remaining products. The yogurt analog with a white color had the highest WHC and γ-tocopherol content. Consumers should be aware of the fact that coconut yogurt alternatives may have nonstandard quality attributes. The differences between such products and yogurt made from cow milk should be explicitly communicated to consumers so that they could make informed purchasing decisions.

Non-Destructive Evaluation of the Physiochemical Properties of Milk Drink Flavored with Date Syrup Utilizing VIS-NIR Spectroscopy and ANN Analysis

A milk drink flavored with date syrup produced at a lab scale level was evaluated. The production process of date syrup involves a sequence of essential unit operations, commencing with the extraction, filtration, and concentration processes from two cultivars: Sukkary and Khlass. Date syrup was then mixed with cow's and camel's milk at four percentages to form a nutritious, natural, sweet, and energy-rich milk drink. The sensory, physical, and chemical characteristics of the milk drinks flavored with date syrup were examined. The objective of this work was to measure the physiochemical properties of date fruits and milk drinks flavored with date syrup, and then to evaluate the physical properties of milk drinks utilizing non-destructive visible-near-infrared spectra (VIS-NIR). The study assessed the characteristics of the milk drink enhanced with date syrup by employing VIS-NIR spectra and utilizing a partial least-square regression (PLSR) and artificial neural network (ANN) analysis. The VIS-NIR spectra proved to be highly effective in estimating the physiochemical attributes of the flavored milk drink. The ANN model outperformed the PLSR model in this context. RMSECV is considered a more reliable indicator of a model's future predictive performance compared to RMSEC, and the R2 value ranged between 0.946 and 0.989. Consequently, non-destructive VIS-NIR technology demonstrates significant promise for accurately predicting and contributing to the entire production process of the product's properties examined.

Microfluidic advances in food safety control

Food contamination is a global concern, particularly in developing countries. Two main types of food contaminants-chemical and biological-are common problems that threaten human health. Therefore, rapid and accurate detection methods are required to address the threat of food contamination. Conventional methods employed to detect these two types of food contaminants have several limitations, including high costs and long analysis time. Alternatively, microfluidic technology, which allows for simple, rapid, and on-site testing, can enable us to control food safety in a timely, cost-effective, simple, and accurate manner. This review summarizes advances in microfluidic approaches to detect contaminants in food. Different detection methods have been applied to microfluidic platforms to identify two main types of contaminants: chemical and biological. For chemical contaminant control, the application of microfluidic approaches for detecting heavy metals, pesticides, antibiotic residues, and other contaminants in food samples is reviewed. Different methods including enzymatic, chemical-based, immunoassay-based, molecular-based, and electrochemical methods for chemical contaminant detection are discussed based on their working principle, the integration in microfluidic platforms, advantages, and limitations. Microfluidic approaches for foodborne pathogen detection, from sample preparation to final detection, are reviewed to identify foodborne pathogens. Common methods for foodborne pathogens screening, namely immunoassay, nucleic acid amplification methods, and other methods are listed and discussed; highlighted examples of recent studies are also reviewed. Challenges and future trends that could be employed in microfluidic design and fabrication process to address the existing limitations for food safety control are also covered. Microfluidic technology is a promising tool for food safety control with high efficiency and applicability. Miniaturization, portability, low cost, and samples and reagents saving make microfluidic devices an ideal choice for on-site detection, especially in low-resource areas. Despite many advantages of microfluidic technology, the wide manufacturing of microfluidic devices still demands intensive studies to be conducted for user-friendly and accurate food safety control. Introduction of recent advances of microfluidic devices will build a comprehensive understanding of the technology and offer comparative analysis for future studies and on-site application.

Potential of ultraviolet laser pulse-induced current for characterizing the grain size of table sugar

In this work, we proposed a laser-induced current (LIC) method to investigate the grain-size dependence of the plasma of table sugar induced by a nanosecond (ns) pulsed ultraviolet laser in the size range of <180 μm->550 μm and achieve the lower power consumption in measurement. Under multiple laser irradiations and an external electric field (Vb) of 200 V, the LIC variation's (ΔIp) standard deviation and variance were 0.53 nA and 0.05 nA, respectively, indicating the relatively small systematic error during the testing process. The Vb causes a decrease in the possibility of electron-ion complexation and accelerates the separation, resulting in an increase in ΔIp with Vb. With increasing grain size (diameter D) of table sugar, ΔI demonstrate a valley-like behaviour and 250-380 μm is the critical range Dc where ΔI is very weak and considerably depends on the Vb with the slope of 0.031 nA/V. At D > 550 μm and Vb = 5 V, ΔI intensities monotonically rise by 30 % when D surpasses Dc. In this instance, the energy was the main contributor to the LIC signal during plasma generation and expansion. While D is less than Dc, ΔIp increases by 27 % at D ≤ 180 μm and Vb = 5 V. The yield stress is the main reason for the formation of plasma with high temperature and density in this situation because the sugar behaves like an elastic solid. The reason for such a LIC variation trend was discussed, which can be explained by considering the morphological, thermal and mechanical properties competing with each other. The present result suggests that the LIC method enables non-contact characterisation of sugar particle size at low-power consumption.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System

In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.

NIR Spectroscopy Assessment of Quality Index of Fermented Milk (Laban) Drink Flavored with Date Syrup during Cold Storage

Fermented milk (laban) with added date syrup can be an excellent candidate for a nutritious drink. Modeling with quality index (Qi) can assist in assessing the quality of the drink’s physiochemical properties. The properties of the laban drink fortified with date syrup were measured and modeled with Qi during shelf life (7 days), and then analyzed with near-infrared spectra (NIR). The aim of this study was to develop a quality index model for the laban drink properties (objective and sensory assessments) and then to predict Qi with a non-destructive measurement of NIR (with partial least-square regression (PLSR) and artificial neural network (ANN) analysis). The results revealed that the developed Qi fits well with measured laban drink properties (viscosity, color, total soluble solids, pH, and sensory assessments during the shelf-life period with R2 = 0.977). The NIR spectrum was efficient to estimate the quality index of the fortified laban drink. It was found that ANN is more appropriate than the PLSR model in estimating the Qi of the Laban drink during cold storage. Thus, non-destructive NIR can predict Qi and can be utilized with great success in the whole chain of production, processing, transportation, storage, and retail market to check the “quality” and “shelf life” of the product.

Scientometric Analysis and Research Mapping Knowledge of Coconut Fibers in Concrete

Biodegradable materials are appropriate for the environment and are gaining immense attention worldwide. The mechanical properties (such as elongation at break, density, and failure strain) of some natural fibers (such as Coir, Hemp, Jute, Ramie, and Sisal) are comparable with those of some synthetic fibers (such as E glass, aramid, or Kevlar). However, the toughness of coconut fibers is comparatively more than other natural fibers. Numerous studies suggest coconut fibers perform better to improve the concrete mechanical properties. However, the knowledge is dispersed, making it difficult for anyone to evaluate the compatibility of coconut fibers in concrete. This study aims to perform a scientometric review of coconut fiber applications in cementitious concrete to discover the various aspects of the literature. The typical conventional review studies are somehow limited in terms of their capacity for linking different literature elements entirely and precisely. Science mapping, co-occurrence, and co-citation are among a few primary challenging points in research at advanced levels. The highly innovative authors/researchers famous for citations, the sources having the highest number of articles, domains that are actively involved, and co-occurrences of keywords in the research on coconut-fiber-reinforced cementitious concrete are explored during the analysis. The bibliometric database with 235 published research studies, which are taken from the Scopus dataset, are analyzed using the VOSviewer application. This research will assist researchers in the development of joint ventures in addition to sharing novel approaches and ideas with the help of a statistical and graphical description of researchers and countries/regions that are contributing. In addition, the applicability of coconut fiber in concrete is explored for mechanical properties considering the literature, and this will benefit new researchers for its use in concrete.