Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Quality Change of Citri Reticulatae Pericarpium (Pericarps of Citrus reticulata 'Chachi') During Storage and Its Sex-Based In Vitro Digestive Performance

Citri Reticulatae Pericarpium (CRP), particularly including the pericarp of Citrus reticulata 'Chachi' (GCP), has been widely used as a food, a dietary supplement, and traditional Chinese medicine. Despite the widespread use of traditional foods, there is limited evidence regarding the precise relationships between storage conditions, aging duration, and the digestive performance of CRP. In this study, the aim was to investigate the impact of the storage conditions on the quality of aged GCP during shelf life and to evaluate the subsequent digestive performance of corresponding GCP decoctions. Respiration in GCP was monitored by measuring oxygen (O2), carbon dioxide (CO2), and methane (CH4) gases throughout the storage simulation, with O2 and CO2 validated as prospective safety measures. Five flavonoids (hesperidin, didymin, nobiletin, tangeretin, and 3,5,6,7,8,3',4'-heptamethoxyflavone) were determined as quality indicators, and their contents were significantly affected by the duration of the storage simulation and the aging periods of GCP. Our study also found that temperature and humidity significantly affected the volatile organic compounds (VOCs) emission from GCP. Eighteen compounds were proposed to show potential as descriptive measures of aging periods while eight compounds were proposed as potential indicators to discriminate among the spoilage level. Furthermore, the bioaccessibility of hesperidin ranged from ~30% to ~50% and was not significantly affected by the GCP's aging time nor the consumer's sex (p < 0.05). This study presents evidence for the future control of the quality of GCP and its digestive performance in males and females.

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

A novel method for vegetable and fruit classification based on using diffusion maps and machine learning

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.

Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision assessing beef freshness

Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.

Emerging applications of nano-optical sensors combined with near-infrared spectroscopy for detecting tea extract fermentation aroma under ultrasound-assisted sonication

The current innovative work combines nano-optical sensors with near-infrared spectroscopy for rapid detection and quantification of polyphenols and investigates the potential of the nano-optical sensor based on chemo-selective colorants to detect the dynamic changes in aroma components during the fermentation of tea extract. The procedure examined the influence of different ultrasound-assisted sonication factors on the changes in the consumption rate of polyphenols during the fermentation of tea extract versus non-sonication as a control group. The results showed that the polyphenol consumption rate improved under the ultrasound conditions of 28 kHz ultrasound frequency, 24 min treatment time, and 40 W/L ultrasonic power density. The metal-organic framework based nano-optical sensors reported here have more adsorption sites for enhanced adsorption of the volatile organic compounds. The polystyrene-acrylic microstructure offered specific surface area for the reactants. Besides, the employed porous silica nanospheres with higher porosity administered improved gas enrichment effect. The nano-optical sensor exhibits good performance with a "chromatogram" for the identification of aroma components in the fermentation process of tea extract. The proposed method respectively enhanced the consumption rate of polyphenol by 35.57%, 11.34% and 16.09% under the optimized conditions. Based on the established polyphenol quantitative prediction models, this work demonstrated the feasibility of using a nano-optical sensor to perform in-situ imaging of the fermentation degree of tea extracts subjected to ultrasonic treatment.