Fast monitoring the dynamic change of total acids during apple vinegar fermentation process using a colorimetric IDA sensor array

Metal Ion-Regulated Fluorescent Sensor Array Based on Gold Nanoclusters for Physiological Phosphate Sensing

The detection of physiological phosphates (PPs) is of great importance due to their essential roles in numerous biological processes, but the efficient detection of different PPs simultaneously remains challenging. In this work, we propose a fluorescence sensor array for detecting PPs based on metal-ion-regulated gold nanoclusters (AuNCs) via an indicator-displacement assay. Zn2+ and Eu3+ are selected to assemble with two different AuNCs, resulting in quenching or enhancing their fluorescence. Based on the competitive interaction of metal ions with AuNCs and PPs, the fluorescence of AuNCs will be recovered owing to the disassembly of AuNC-metal ion ensembles. Depending on different PPs' distinct fluorescence responses, a four-channel sensor array was established. The array not only exhibits good discrimination capability for eight kinds of PPs (i.e., ATP, ADP, AMP, GTP, CTP, UTP, PPi, and Pi) via linear discriminant analysis but also enables quantitative detection of single phosphate (e.g., ATP) in the presence of interfering PPs mixtures. Moreover, potential application of the present sensor array for the discrimination of different PPs in real samples (e.g., cell lysates and serum) was successfully demonstrated with a good performance. This work illustrates the great potential of a metal ion-regulated sensor array as a new and efficient sensing platform for differential sensing of phosphates as well as other disease-related biomolecules.

Study on the Characteristics of Vacuum-Bagged Fermentation of Apo Pickle and Visualization Array Analysis of the Fermentation Process

Apo pickle is a fermented food with a long edible history in the Jiangnan region of China. Traditionally, plastic bottles are used as Apo pickle's fermentation containers, and artificial bottling costs are high. The goal of this study is to compare the fermentation effects of Apo pickle fermented under low pressure in a vacuum bag (VBA) and Apo pickle fermented under normal pressure in plastic bottles (TBA) to determine the feasibility of fermenting Apo pickle in a vacuum bag rather than a plastic bottle, thereby lowering production costs. At the same time, a gas-sensitive colorimetric sensor array (CSA) was developed to distinguish different fermentation stages of Apo pickle. The results revealed that the main genera in the initial and final phases of Apo pickle fermentation were Weissella and Lactobacillus, unaffected by fermentation containers. At the same fermentation time, the abundance of Lactobacillus and the content of flavor substances in VBA were higher, and the fermentation speed of VBA was faster at 0-15 d, so a vacuum bag could be used instead of a plastic bottle. The CSA could discriminate between different fermentation procedures of Apo pickles with an accuracy rate of 93.8%. Its principle is similar to that of an electronic nose. It has the advantages of convenience, rapidity, and no need for professional equipment, so it can be used as a new method to judge the fermentation degree of apo pickle.

Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

Cost-effective colorimetric sensor for authentication of protected designation of origin (PDO) Longjing green tea

Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products.

Indicator displacement assay for freshness monitoring of green tea during storage

Aging of green tea leads to reductions in its flavor and health value, yet in situ testing methods for green tea freshness are lacking. A novel sensitive indicator displacement assay (IDA) sensor was constructed and applied for monitoring of green tea freshness during storage. Low-cost pH dyes and metal ions were used as indicators and receptors, respectively, for the targeted detection of catechins in tea samples. The feasibility of the IDA reaction was verified using images and UV-vis spectroscopy, respectively. IDA combined with supervised algorithms achieved accurate identification of green tea freshness with an accuracy of 86.67%, and acceptable accuracies in the prediction of catechin monomers and total catechins with ratio of prediction to deviation values over 1.5. Thus, the developed IDA sensor is capable of qualitative and quantitative monitoring of the green tea freshness during storage, providing a new option for quality evaluation and control of green teas.