Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness.

BACKGROUND: Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies. RESULTS: Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples. CONCLUSION: The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.

Identification of volatile compounds and metabolic pathway during ultrasound-assisted kombucha fermentation by HS-SPME-GC/MS combined with metabolomic analysis.

The current work combines headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) with multivariate analysis fusion metabonomics for examining metabolite profile changes. The correlation with metabolic pathways during the fermentation of kombucha tea were comprehensively explored. For optimizing the fermentation process, ultrasound-assisted factors were explored. A total of 132 metabolites released by fermented kombucha were detected by HS-SPME-GC/MS. We employed the principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to present the relationship between aroma components and fermentation time, of which the first two principal components respectively accounted for 60.3% and 6.5% of the total variance. Multivariate statistical analysis showed that during the fermentation of kombucha tea, there were significant differences in the phenotypes of metabolites in the samples, and 25 characteristic metabolites were selected as biomarkers. Leaf alcohol was first proposed as the characteristic volatile in the fermentation process of kombucha. Furthermore, we addressed the generation pathways of characteristic volatiles, their formation mechanisms, and the transformational correlation among them. Our findings provide a roadmap for future kombucha fermentation processing to enhance kombucha flavor and aroma.

Overview of advanced technologies for volatile organic compounds measurement in food quality and safety.

Food quality and nutrition have received much attention in recent decades, thanks to changes in consumer behavior and gradual increases in food consumption. The demand for high-quality food necessitates stringent quality assurance and process control measures. As a result, appropriate analytical tools are required to assess the quality of food and food products. VOCs analysis techniques may meet these needs because they are nondestructive, convenient to use, require little or no sample preparation, and are environmentally friendly. In this article, the main VOCs released from various foods during transportation, storage, and processing were reviewed. The principles of the most common VOCs analysis techniques, such as electronic nose, colorimetric sensor array, migration spectrum, infrared and laser spectroscopy, were discussed, as well as the most recent research in the field of food quality and safety evaluation. In particular, we described data processing algorithms and data analysis captured by these techniques in detail. Finally, the challenges and opportunities of these VOCs analysis techniques in food quality analysis were discussed, as well as future development trends and prospects of this field.

Detection of wheat toxigenic Aspergillus flavus based on nano-composite colorimetric sensing technology.

Volatile organic compounds (VOCs) are an important indicator for fungal-infected wheat identification. This work proposes a novel approach for toxigenic Aspergillus flavus infected wheat identification through characteristic VOCs analyzed by nano-composite colorimetric sensors. Nanoparticles of poly styrene-co-acrylic acid (PSA), porous silica nanoparticles (PSN), and metal-organic framework (MOF) were combined with boron dipyrromethene (BODIPY) to fabricate nano-composite colorimetric sensors. The combination mechanisms for nanoparticles and the information extracted from nano-colorimetric sensors by digital images were analyzed in the current work. Furthermore, linear discriminant analysis (LDA) and k-nearest neighbor (KNN) were used comparatively to analyze the data from images, and toxigenic Aspergillus flavus infected wheat samples could be 100.00% correctly identified when using the optimal KNN model. This research contributes to the practical analysis of VOCs and the detection of toxigenic Aspergillus flavus infected wheat.

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.

Freshness Identification of Oysters Based on Colorimetric Sensor Array Combined with Image Processing and Visible Near-Infrared Spectroscopy.

Volatile organic compounds (VOCs) could be used as an indicator of the freshness of oysters. However, traditional characterization methods for VOCs have some disadvantages, such as having a high instrument cost, cumbersome pretreatment, and being time consuming. In this work, a fast and non-destructive method based on colorimetric sensor array (CSA) and visible near-infrared spectroscopy (VNIRS) was established to identify the freshness of oysters. Firstly, four color-sensitive dyes, which were sensitive to VOCs of oysters, were selected, and they were printed on a silica gel plate to obtain a CSA. Secondly, a charge coupled device (CCD) camera was used to obtain the "before" and "after" image of CSA. Thirdly, VNIS system obtained the reflected spectrum data of the CSA, which can not only obtain the color change information before and after the reaction of the CSA with the VOCs of oysters, but also reflect the changes in the internal structure of color-sensitive materials after the reaction of oysters' VOCs. The pattern recognition results of VNIS data showed that the fresh oysters and stale oysters could be separated directly from the principal component analysis (PCA) score plot, and linear discriminant analysis (LDA) model based on variables selection methods could obtain a good performance for the freshness detection of oysters, and the recognition rate of the calibration set was 100%, while the recognition rate of the prediction set was 97.22%. The result demonstrated that the CSA, combined with VNIRS, showed great potential for VOCS measurement, and this research result provided a fast and nondestructive identification method for the freshness identification of oysters.

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

Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.

Quantitative analysis of colony number in mouldy wheat based on near infrared spectroscopy combined with colorimetric sensor.

Current work presented a novel method based on colorimetric sensor (CS) combined with visible/near-infrared spectroscopy (VNIRs) for the detection of volatile markers in wheat infected by Aspergillus glaucus. Wheat samples with different mouldy degree was cultivated for backup under temperature of 25-28 °C in incubator. The total colony number was determined by flat colony counting method. Through employing chemo-responsive dyes including 8-(4-nitrophenyl)-4, 4-difluoro-BODIPY (NO2BDP), 8-(4-bromophenyl)-4,4-difluoro-BODIPY(BrBDP) and 8-phenyl-4,4-difluoro- BODIPY(HBDP) as capture probes of colorimetric sensor for volatile organic compounds (VOCs). The spectral data of CS-VNIRs were scanned and used to build synergic interval partial least squares (Si-PLS) models. The optimized Si-PLS model based on HBDP sensor gave a better detection performance, and the correlation coefficient of the prediction set Rp = 0.9387. The achieved high correlation rates imply that the technique may be deployed as a panacea to identify and quantify the colony number of different mouldy wheat.

Discrimination of Chinese Baijiu grades based on colorimetric sensor arrays.

In this study, a novel colorimetric sensor array based on chemo dyes including porphyrins and pH indicators were developed to analyse the volatile organic compounds of Chinese Baijiu with different grades. Ethyl acetate, ethyl butyrate and ethyl caproate appeared by significantly different concentration in different Baijiu grades measuring by gas chromatography and mass spectrometry and they were chosen as characteristic volatile organic components. The olfactory visualization system based on colorimetric sensor arrays was used to identify different Baijiu grades. The data were processed by building the principle components analysis, linear discriminant analysis and K-nearest neighbor classification models with the results of sensory evaluation and olfactory visualization system. This work presents a new-style colorimetric sensor using sensitive chemo dyes which has significant potential in quantitative analysis of volatile organic compounds, afterwards identifying different grades of Baijiu.

A novel colorimetric sensor array based on boron-dipyrromethene dyes for monitoring the storage time of rice.

A novel colorimetric sensor array based on boron-dipyrromethene (BODIPY) dyes was developed to monitor the volatile organic compounds (VOCs) of rice at different storage times. The VOCs of rice at different storage times were analyzed through GC-MS combined with multivariate analysis, and the compound 18-crown-6 was found significantly changed during rice aging process. Aimed at 18-crown-6 with particular macrocyclic structure, a series of BODIPYs were targeted synthesized for the selection of sensitive chemically responsive dyes. Four dyes were chosen to construct colorimetric sensor array based on sensitivity to VOCs of aged rice samples. Data acquired from the interactions of dyes and rice VOCs were subjected to the principal components analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained by the LDA model was 98.75% in prediction set. Application of BODIPYs in this work has improved the sensitivity and expanded the choices of colorimetric dyes for the specific detection.