Prediction of storage quality of fresh-cut green peppers using artificial neural network

Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables

Fruits and vegetables are valued for their flavor and high nutritional content, but their perishability and seasonality present challenges for storage and marketing. To address these, it is essential to accurately monitor their quality and predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, and builds predictive models to estimate food shelf life. These models can be continuously refined with new data, improving accuracy and robustness over time. This article discusses key machine learning methods for predicting shelf life and quality control of fruits and vegetables, with a focus on storage conditions, physicochemical properties, and non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, and integration of deep learning and non-destructive testing. These developments aim to reduce resource waste, provide theoretical basis and technical guidance for the formation of modern intelligent agricultural supply chains, promote sustainable green development of the food industry, and foster interdisciplinary integration in the field of artificial intelligence.

Fresh-Cut Bell Peppers in Modified Atmosphere Packaging: Improving Shelf Life to Answer Food Security Concerns

The influence of modified atmosphere packaging (MAP, 10% O₂ and 45% CO₂) on the quality characteristics of fresh-cut green, red and yellow bell peppers (Capsicum annuum L. var annuum) was investigated. Packaging film bags (Krehalon MLF40-PA/PE) with fresh-cut bell peppers were stored for up to 17 days at 5 °C. The in-package O₂ level ranged between 10 and 15%, respecting the current recommendations for fresh-cut vegetable products. Initial CO₂ levels were higher than commonly used (from 5 to 10%), decreasing progressively over time due to the permeability of the selected polyethylene film. At the end of the storage period, they stabilized between 2 and 5%. A small variation in texture, moisture, titratable acidity, pH and microbial growth was observed during the storage period, as well as a good color retention and sensory properties maintenance. Negligible losses in the antioxidant activity and bioactive compounds (total phenol, flavonoid, anthocyanin and carotenoid content) were noted at the end of the study. Sensory analysis showed that panelists could not detect significant differences among sampling periods. A PCA with predictive biplots confirmed the existence of significant correlations. The products retain their initial characteristics without severe loss of quality until at least the 17th storage day. Given the current commercial shelf life of fresh-cut bell peppers, ranging from 9 to 14 days, the described treatment enabled an increase of at least 3 days (20%) of the products shelf life, reducing food waste and contributing to food security.

A new methodology for sensory quality assessment of garlic based on metabolomics and an artificial neural network

This study has established a new method for the sensory quality determination of garlic and garlic products on the basis of metabolomics and an artificial neural network. A total of 89 quality indicators were obtained, mainly through the metabolomics analysis using gas chromatography/mass spectrometry (GC/MS) and high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The quality indicator data were standardized and fused at a low level, and then seven representative indicators including the a* (redness) value, and the contents of S-methyl-l-cysteine, 3-vinyl-1,2-dithiacyclohex-5-ene, glutamic acid, l-tyrosine, d-fructose and propene were screened by partial least squares discriminant analysis (PLS-DA), analysis of variance (ANOVA) and correlation analysis (CA). Subsequently, the seven representative indicators were employed as the input data, while the sensory scores for the garlic obtained by a traditional sensory evaluation were regarded as the output data. A back propagation artificial neural network (BPANN) model was constructed for predicting the sensory quality of garlic from four different areas in China. The R² value of the linear regression equation between the predicted scores and the traditional sensory scores for the garlic was 0.9866, with a mean square error of 0.0034, indicating that the fitting degree was high and that the BPANN model built in this study could predict the sensory quality of garlic accurately. In general, the method developed in this study for the sensory quality determination of garlic and garlic products is rapid, simple and efficient, and can be considered as a potential method for application in quality control in the food industry.

This study has established a new method for the sensory quality determination of garlic and garlic products on the basis of metabolomics and an artificial neural network.

Prediction of benzo[a]pyrene content of smoked sausage using back-propagation artificial neural network

BACKGROUND

Benzo[a]pyrene (BaP), a potent mutagen and carcinogen, is reported to be present in processed meat products and, in particular, in smoked meat. However, few methods exist for predictive determination of the BaP content of smoked meats such as sausage. In this study, an artificial neural network (ANN) model based on the back-propagation (BP) algorithm was used to predict the BaP content of smoked sausage.

RESULTS

The results showed that the BP network based on the Levenberg-Marquardt algorithm was the best suited for creating a nonlinear map between the input and output parameters. The optimal network structure was 3-7-1 and the learning rate was 0.6. This BP-ANN model allowed for accurate predictions, with the correlation coefficients (R) for the experimentally determined training, validation, test and global data sets being 0.94, 0.96, 0.95 and 0.95 respectively. The validation performance was 0.013, suggesting that the proposed BP-ANN may be used to predictively detect the BaP content of smoked meat products.

CONCLUSION

An effective predictive model was constructed for estimation of the BaP content of smoked sausage using ANN modeling techniques, which shows potential to predict the BaP content in smoked sausage. © 2017 Society of Chemical Industry.

Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies

Shelf life of fresh fruits and vegetables is greatly influenced by environmental conditions. Increasing temperature usually results in accelerated loss of quality and shelf-life reduction, which is not physically visible until too late in the supply chain to adjust logistics to match shelf life. A blackberry study showed that temperatures inside pallets varied significantly and 57% of the berries arriving at the packinghouse did not have enough remaining shelf life for the longest supply routes. Yet, the advanced shelf-life loss was not physically visible. Some of those pallets would be sent on longer supply routes than necessary, creating avoidable waste. Other studies showed that variable pre-cooling at the centre of pallets resulted in physically invisible uneven shelf life. We have shown that using simple temperature measurements much waste can be avoided using 'first expiring first out'. Results from our studies showed that shelf-life prediction should not be based on a single quality factor as, depending on the temperature history, the quality attribute that limits shelf life may vary. Finally, methods to use air temperature to predict product temperature for highest shelf-life prediction accuracy in the absence of individual sensors for each monitored product have been developed. Our results show a significant reduction of up to 98% in the root-mean-square-error difference between the product temperature and air temperature when advanced estimation methods are used.