Winter jujube (Zizyphus jujuba Mill.) quality forecasting method based on electronic nose

Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology

Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on Atractylodes macrocephala, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.

Effects of cold plasma, high hydrostatic pressure, ultrasound, and high-pressure carbon dioxide pretreatments on the quality characteristics of vacuum freeze-dried jujube slices

Pretreatment combined with vacuum freeze-drying is an effective technique to extend the storage period of jujube fruits and reduce energy consumption and cost; however, the effects of pretreatment on the quality characteristics of jujube during vacuum freeze-drying remain unknown. In this study, the effects of cold plasma (CP), high hydrostatic pressure (HHP), ultrasound (US), high-pressure carbon dioxide (HPCD), and conventional blanching (BC) as pretreatments on the performance of vacuum freeze-dried jujube slices were investigated. The results indicated that the application of different pretreatments decreased the water activity and increased the rehydration capacity, owing to the pretreatment etching larger and more porous holes in the microstructure. Freeze-dried jujube slices pretreated with HPCD retained most of their quality characteristics (color, hardness, and volatile compounds), followed by the HHP- and US-pretreated samples, whereas samples pretreated with BC showed the greatest deterioration in quality characteristics, and hence, BC is not recommended as a pretreatment for freeze-dried jujube slices. Sensory evaluation based on hedonic analysis showed that jujube slices pretreated with HPCD and US were close to the control sample and scored highest. Compared to other pretreated samples and the control, freeze-dried jujube slices pretreated with HPCD showed the least degradation (4.93%) of cyclic adenosine monophosphate (cAMP), the highest contents of total phenol, total flavonoid, and l-ascorbic acid, and the highest antioxidant capacity. Partial least squares-discriminant analysis (PLS-DA) was performed to screen all the quality characteristic data of different pretreated samples, and 12 volatile compounds, including ethyl hexanoate and (E)-2-hexenal, along with color, l-ascorbic acid content, and cAMP content were found suitable to be used as discriminators for pretreated freeze-dried jujube slices. Therefore, non-thermal pretreatments, including HPCD, US, and HHP pretreatments, are promising techniques for the vacuum freeze-drying of jujube products.

Recent Advances in pH-Responsive Freshness Indicators Using Natural Food Colorants to Monitor Food Freshness

Recently, due to the enhancement in consumer awareness of food safety, considerable attention has been paid to intelligent packaging that displays the quality status of food through color changes. Natural food colorants show useful functionalities (antibacterial and antioxidant activities) and obvious color changes due to their structural changes in different acid and alkali environments, which could be applied to detect these acid and alkali environments, especially in the preparation of intelligent packaging. This review introduces the latest research on the progress of pH-responsive freshness indicators based on natural food colorants and biodegradable polymers for monitoring packaged food quality. Additionally, the current methods of detecting food freshness, the preparation methods for pH-responsive freshness indicators, and their applications for detecting the freshness of perishable food are highlighted. Subsequently, this review addresses the challenges and prospects of pH-responsive freshness indicators in food packaging, to assist in promoting their commercial application.

Characterization and Analysis of Okoume and Aiele Essential Oils from Gabon by GC-MS, Electronic Nose, and Their Antibacterial Activity Assessment

Essential oil resins of Aucoumea klaineana (Okoume) and Canarium schweinfurthii (Aiele) species, of the Burseraceae family, were studied to investigate their bioactive constituents and their antibacterial activities. Aiele resin had a higher yield (6.86%) of essential oil than Okoume (3.62%). Twenty-one compounds for Okoume and eighteen for Aiele essential oil were identified using a gas chromatography-mass spectrometry (Gp-C-MS) technique. The main compounds identified in Okoume essential oil were benzenemethanol, α, α,4-trimethyl (28.85%), (+)-3-carene (3,7,7-trimethyl bicyclo[4.1.0]hept-3-ene) (17.93%), D-Limonene ((4R)-1-methyl-4-prop-1-en-2-ylcyclohexene) (19.36%). With regard to the Aiele essential oil, we identified (1R,4S)-1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.64%), and 1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.83%). Two strains of bacteria, Escherichia coli and Staphylococcus aureus, were used in antibacterial tests. S. aureus was found to be more sensitive to Okoume and Aiele essential oils, with a high inhibition zone ranging from 20 to 16 mm. In comparison, the inhibition zone ranged from 6 to 12 mm for E. coli. An electronic nose (e-nose) combined with pattern analysis methods such as principal component analysis (PCA), discriminant function analysis (DFA), and hierarchical cluster analysis (HCA) were used to discriminate the essential oil samples. In summary, the e-nose and GC-MS allowed the identification of bioactive compounds in the essential oil samples, which have a strong antimicrobial activity, with satisfactory results.

Calcium maintained higher quality and enhanced resistance against chilling stress by regulating enzymes in reactive oxygen and biofilm metabolism of Chinese winter jujube fruit

The postharvest senescence of Chinese winter jujube fruit can be effectively delayed by refrigerated storage. However, chilling injury often occurs in jujube fruit during cold storage. In this study, Chinese winter jujubes were sprayed with CaCl₂ (4%) 3 times at intervals of 2 hr on the day of refrigeration. The results presented that maximum difference of 2.7 N firmness, 3.42% TAC, and 0.8 OD₂₈₀ /g polyphenol content were detected in calcium-treated fruit during cold storage, but the levels of O 2 - , MDA, hydrogen peroxide, browning rate, electrolyte leakage, and weight loss rate were significantly inhibited (p < .05). The maximum difference of enzymes activity of CAT, POD, SOD was 2.1, 10.8, and 40.6 mol h^-1 kg^-1 respectively, but 21.1 mol h^-1 kg^-1 PPO was restrained in the treated group. In conclusion, the results provided a reliable method for inhibiting cold injury and explained the internal molecular mechanism of the fruit regulated by calcium. PRACTICAL APPLICATIONS: Refrigerated storage is an important method for extending the storage time of Chinese winter jujube fruit. However, cold damage may occur when the jujubes are stored at low temperature for long-term. It is, therefore, of great significance to find a new method and reveal the molecular mechanism. We believe that our study makes a significant contribution to the literature because it provides an effective method of maintaining higher quality and mechanistic insights into the resistance against the chilling stress of jujubes.

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

Development of Novel Electronic Nose Applied for Strawberry Freshness Detection during Storage

A newly self-developed electronic nose (E-nose) system for the detection of “Hongyan” strawberry freshness at different storage periods was studied. The system consisted of six metal oxide semiconductor sensors connected to a data acquisition system and a computer with pattern recognition software. The aroma emitted by “Hongyan” strawberry samples was detected during post-harvesting storage, and stable E-nose response values were used to develop cluster analysis and classification models. The successive projections algorithm was employed to optimize the sensors array, and the results obtained by gas chromatography–mass spectrometry analysis proved that the optimized sensor array was feasible to differentiate decayed strawberries from fresh ones. Partial least squares discriminant analysis and support vector machine (SVM) models were built. Accuracy of 94.9 % on the testing set was obtained based on the optimized sensor array, and this result was satisfactory compared to that of commercial PEN3 E-nose.

Ultrasensitive electrochemical immuno-sensing platform based on gold nanoparticles triggering chlorpyrifos detection in fruits and vegetables

Chlorpyrifos (chl) is an organophosphate pesticide extensively used in agriculture and highly toxic for human health. Fluorine doped tin-oxide (FTO) based electrochemical nanosensor was developed for chlorpyrifos detection with gold nanoparticles (AuNPs) and anti-chlorpyrifos antibodies (chl-Ab). AuNPs provides high electrical conductivity and specific resistivity, thus increases the sensitivity of immunoassay. High electrical conductivity of AuNPs reveals that it promotes the redox reaction for better cyclic voltammetry. Based on the intrinsic conductive properties of FTO-AuNPs complex, chl-Ab was immobilized onto AuNPs surface. Under optimized conditions, the proposed FTO based nanosensor exhibited high sensitivity and stable response for the detection of chlorpyrifos, ranging from 1fM to 1µM with limit of detection (LOD) up to 10fM. The FTO-AuNPs sensor was successfully employed for the detection of chlorpyrifos in standard as well in real samples up to 10nM for apple and cabbage, 50nM for pomegranate. The proposed FTO-AuNPs nanosensor can be used as a quantitative tool for rapid, on-site detection of chlorpyrifos traces in real samples when miniaturized due to its excellent stability, sensitivity, and simplicity.

The fungicide triadimefon affects beer flavor and composition by influencing Saccharomyces cerevisiae metabolism

Despite the fact that beer is produced on a large scale, the effects of pesticide residues on beer have been rarely investigated. In this study, we used micro-brewing settings to determine the effect of triadimefon on the growth of Saccharomyces cerevisiae and beer flavor. The yeast growth in medium was significantly inhibited (45%) at concentrations higher than 5 mg L(-1), reaching 80% and 100% inhibition at 10 mg L(-1) and 50 mg L(-1), respectively. There were significant differences in sensory quality between beer samples fermented with and without triadimefon based on data obtained with an electronic tongue and nose. Such an effect was most likely underlain by changes in yeast fermentation activity, including decreased utilization of maltotriose and most amino acids, reduced production of isobutyl and isoamyl alcohols, and increased ethyl acetate content in the fungicide treated samples. Furthermore, yeast metabolic profiling by phenotype microarray and UPLC/TOF-MS showed that triadimefon caused significant changes in the metabolism of glutathione, phenylalanine and sphingolipids, and in sterol biosynthesis. Thus, triadimefon negatively affects beer sensory qualities by influencing the metabolic activity of S. cerevisiae during fermentation, emphasizing the necessity of stricter control over fungicide residues in brewing by the food industry.

Effect of Se treatment on the volatile compounds in broccoli

Broccoli contains high levels of bioactive compounds but deteriorates and senesces easily. In the present study, freshly harvested broccoli was treated with selenite and stored at two different temperatures. The effect of selenite treatment on sensory quality and postharvest physiology were analyzed. Volatile components were assessed by HS-SPME combined with GC-MS and EN. The metabolism of Se and S was also examined. Results indicated that Se treatment had a significant effect on maintaining the sensory quality, suppressing the respiration intensity and ethylene production, as well as increasing the content of Se and decreasing the content of S. In particular, significant differences in the composition of volatile compounds were present between control and Se-treated. The differences were mainly due to differences in alcohols and sulfide compounds. These results demonstrate that Se treatment can have a positive effect on maintaining quality and enhancing its sensory quality through the release of volatile compounds.

Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds. An ad-hoc electronic nose (EN) system was implemented to sense the samples and acquire signals. Forty-nine features were extracted from the EN sensor matrix (seven parameters to describe each sensor curve response). The extracted features were ordered in relevance by the intra/inter variance criterion (Vr), also known as the Fisher discriminant. A leave-one-out cross validation technique was implemented for estimating the classification accuracy reached by both algorithms. Success rates were calculated using 10, 20, 30, and the entire selected features from the response of the sensor array. The results revealed a maximum classification accuracy of 99% when applying the Fuzzy ARTMAP algorithm and 82% for LDA, using the first 10 features in both cases. Further classification results explained that sub-optimal performance is likely to occur when all the response features are applied. It was found that an electronic nose system employing a Fuzzy ARTMAP classifier could become an accurate, easy, and inexpensive alternative tool for qualitative control in the production of Rosa damascene essential oil.

Is it possible to rapidly and noninvasively identify different plants from Asteraceae using electronic nose with multiple mathematical algorithms?

Many plants originating from the Asteraceae family are applied as herbal medicines and also beverage ingredients in Asian areas, particularly in China. However, they may be confused due to their similar odor, especially when ground into powder, losing their typical macroscopic characteristics. In this paper, 11 different multiple mathematical algorithms, which are commonly used in data processing, were utilized and compared to analyze the electronic nose (E-nose) response signals of different plants from Asteraceae family. Results demonstrate that three-dimensional plot scatter figure of principal component analysis with less extracted components could offer the identification results more visually; simultaneously, all nine kinds of artificial neural network could give classification accuracies at 100%. This paper presents a rapid, accurate, and effective method to distinguish Asteraceae plants based on their response signals in E-nose. It also gives insights to further studies, such as to find unique sensors that are more sensitive and exclusive to volatile components in Chinese herbal medicines and to improve the identification ability of E-nose. Screening sensors made by other novel materials would be also an interesting way to improve identification capability of E-nose.

Penaeus orientolis prawn freshness rapid determination method based on electronic nose and non-linear stochastic resonance technique

In this paper, Penaeus orientolis prawn freshness rapid determination method using electronic nose (e-nose) and non-linear data processing technique is studied. E-nose responses to prawns stored at 4 °C are measured. Meanwhile, physical/chemical indexes (firmness, pH, total volatile basic nitrogen (TVB-N), total viable count (TVC), and human sensory evaluation) are examined to provide freshness references for e-nose analysis. E-nose measurement data is analyzed by principal component analysis (PCA), stochastic resonance (SR), and double-layered cascaded serial stochastic resonance (DCSSR). PCA partially discriminates prawns under different storage time. SR and DCSSR signal-to-noise ratio (SNR) spectrum eigen values discriminate prawns successfully. Multi-variables regressions (MVR) are conducted between physical/chemical indexes and SR/DCSSR output SNR minimal (SNR-Min) values. Results indicate that SNR-Min values present more significant linearity relation with physical/chemical indexes. Prawn freshness forecasting model is developed via Harris fitting regression on DCSSR SNR-Min values. Validating experiments demonstrate that forecasting accuracy of this model is 94.29%.

Electronic-nose applications for fruit identification, ripeness and quality grading

Fruits produce a wide range of volatile organic compounds that impart their characteristically distinct aromas and contribute to unique flavor characteristics. Fruit aroma and flavor characteristics are of key importance in determining consumer acceptance in commercial fruit markets based on individual preference. Fruit producers, suppliers and retailers traditionally utilize and rely on human testers or panels to evaluate fruit quality and aroma characters for assessing fruit salability in fresh markets. We explore the current and potential utilization of electronic-nose devices (with specialized sensor arrays), instruments that are very effective in discriminating complex mixtures of fruit volatiles, as new effective tools for more efficient fruit aroma analyses to replace conventional expensive methods used in fruit aroma assessments. We review the chemical nature of fruit volatiles during all stages of the agro-fruit production process, describe some of the more important applications that electronic nose (e-nose) technologies have provided for fruit aroma characterizations, and summarize recent research providing e-nose data on the effectiveness of these specialized gas-sensing instruments for fruit identifications, cultivar discriminations, ripeness assessments and fruit grading for assuring fruit quality in commercial markets.