Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries

AI-based processing of future prepared foods: Progress and prospects

The prepared foods sector has grown rapidly in recent years, driven by the fast pace of modern living and increasing consumer demand for convenience. Prepared foods are taking an increasingly important role in the modern catering industry due to their ease of storage, transportation, and operation. However, their processing faces several challenges, including labor shortages, inefficient sorting, inadequate cleaning, unsafe cutting processes, and a lack of industry standards. The development of artificial intelligence (AI) will change the processing of prepared foods. This review summarizes the progress and prospects of AI applications in the sorting/classification, cleaning, cutting, preprocessing, and freezing of prepared foods, encompassing techniques such as mathematical modeling, chemometrics, machine learning, fuzzy logic, and adaptive neuro fuzzy inference system. For example, AI-powered sorting systems using computer vision have improved accuracy in ingredient classification, while deep learning models in cleaning processes have enhanced microbial contamination detection with high spectral imaging techniques. Despite challenges like managing large-scale data and complex models, AI has shown significant potential to inspire both industry practice and research. AI applications can enhance the efficiency, accuracy, and consistency of prepared foods processing, while also reducing labor costs, improving hygiene monitoring, minimizing resource waste, and decreasing environmental impact. Furthermore, AI-driven resource optimization has demonstrated its potential in reducing energy consumption and promoting sustainable food production practices. In the future, AI technology is expected to further improve model generalization and operation precision, driving the food processing industry toward smarter, more sustainable development. This study provides valuable insights to encourage further innovation in AI applications within food processing and technological advancement in the food industry.

Deep learning-assisted flavonoid-based fluorescent sensor array for the nondestructive detection of meat freshness

Gas sensors containing indicators have been widely used in meat freshness testing. However, concerns about the toxicity of indicators have prevented their commercialization. Here, we prepared three fluorescent sensors by complexing each flavonoid (fisetin, puerarin, daidzein) with a flexible film, forming a fluorescent sensor array. The fluorescent sensor array was used as a freshness indication label for packaged meat. Then, the images of the indication labels on the packaged meat under different freshness levels were collected by smartphones. A deep convolutional neural network (DCNN) model was built using the collected indicator label images and freshness labels as the dataset. Finally, the model was used to detect the freshness of meat samples, and the overall accuracy of the prediction model was as high as 97.1%. Unlike the TVB-N measurement, this method provides a nondestructive, real-time measurement of meat freshness.

Berries as Foods: Processing, Products, and Health Implications

Berries are highly regarded as flavorful and healthy fruits that may prevent or delay some chronic diseases attributed to oxidative stress and inflammation. Berries are low in calories and harbor diverse bioactive phytochemicals, antioxidants, dietary fibers, and vitamins. This review delves into the main characteristics of fresh berries and berry products as foods and the technologies associated with their production. The main effects of processing operations and related variables on bioactive components and antioxidants are described. This review critically discusses why some health claims based on in vitro antioxidant data and clinical studies and intervention trials are difficult to assess. The review suggests that the beneficial health effects of berries are derived from a multifactorial combination of complex mixtures of abundant phenolic components, antioxidants, and their metabolites acting synergistically or additively with other nutrients like fibers and vitamins and possibly by modulating the gut microbiota.

Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review

The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.

Sea Buckthorn Pretreatment, Drying, and Processing of High-Quality Products: Current Status and Trends

Sea buckthorn is a kind of berry rich in nutritional and industrial value. Due to its thin skin, juicy pulp, and short shelf life, it is usually preserved via freezing methods or directly processed into sea buckthorn puree after harvest. It can also be dried and processed into products such as dried sea buckthorn fruit, freeze-dried sea buckthorn powder, and sea buckthorn oil. This review, therefore, provides an overview of the existing state of drying and high-quality processing of sea buckthorn. The effects of different pretreatment and drying techniques on the drying characteristics and quality of sea buckthorn and the existing problems of superior-quality processing of sea buckthorn products are summarised. The development trend of sea buckthorn drying methods and the ways to achieve high-quality processing of sea buckthorn products are indicated. These ways are mainly related to the following: (1) The application of combined pretreatment and drying techniques to find a balance between economy, ecology, and efficiency; (2) Introducing new online measurement and control technology into drying equipment; (3) Optimising the existing process to form a complete sea buckthorn industrial chain and develop the sea buckthorn deep-processing industry.

Nano and Technological Frontiers as a Sustainable Platform for Postharvest Preservation of Berry Fruits

Berries are highly perishable and susceptible to spoilage, resulting in significant food and economic losses. The use of chemicals in traditional postharvest protection techniques can harm both human health and the environment. Consequently, there is an increasing interest in creating environmentally friendly solutions for postharvest protection. This article discusses various approaches, including the use of "green" chemical compounds such as ozone and peracetic acid, biocontrol agents, physical treatments, and modern technologies such as the use of nanostructures and molecular tools. The potential of these alternatives is evaluated in terms of their effect on microbial growth, nutritional value, and physicochemical and sensorial properties of the berries. Moreover, the development of nanotechnology, molecular biology, and artificial intelligence offers a wide range of opportunities to develop formulations using nanostructures, improving the functionality of the coatings by enhancing their physicochemical and antimicrobial properties and providing protection to bioactive compounds. Some challenges remain for their implementation into the food industry such as scale-up and regulatory policies. However, the use of sustainable postharvest protection methods can help to reduce the negative impacts of chemical treatments and improve the availability of safe and quality berries.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

Truths and myths about superfoods in the era of the COVID-19 pandemic

Nowadays, during the current COVID-19 pandemic, consumers increasingly seek foods that not only fulfill the basic need (i.e., satisfying hunger) but also enhance human health and well-being. As a result, more attention has been given to some kinds of foods, termed "superfoods," making big claims about their richness in valuable nutrients and bioactive compounds as well as their capability to prevent illness, reinforcing the human immune system, and improve overall health.This review is an attempt to uncover truths and myths about superfoods by giving examples of the most popular foods (e.g., berries, pomegranates, watermelon, olive, green tea, several seeds and nuts, honey, salmon, and camel milk, among many others) that are commonly reported as having unique nutritional, nutraceutical, and functional characteristics.While superfoods have become a popular buzzword in blog articles and social media posts, scientific publications are still relatively marginal. The reviewed findings show that COVID-19 has become a significant driver for superfoods consumption. Food Industry 4.0 innovations have revolutionized many sectors of food technologies, including the manufacturing of functional foods, offering new opportunities to improve the sensory and nutritional quality of such foods. Although many food products have been considered superfoods and intensively sought by consumers, scientific evidence for their beneficial effectiveness and their "superpower" are yet to be provided. Therefore, more research and collaboration between researchers, industry, consumers, and policymakers are still needed to differentiate facts from marketing gimmicks and promote human health and nutrition.

Rapid and Non-Destructive Techniques for the Discrimination of Ripening Stages in Candonga Strawberries

Electronic nose (e-nose), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and image analysis (IA) were used to discriminate the ripening stage (half-red or red) of strawberries (cv Sabrosa, commercially named Candonga), harvested at three different times (H1, H2 and H3). Principal component analysis (PCA) performed on the e-nose, ATR-FTIR and IA data allowed us to clearly discriminate samples based on the ripening stage, as in the score space they clustered in distinct regions of the plot. Moreover, a correlation analysis between the e-nose sensor and 57 volatile organic compounds (VOCs), which were overall detected in all the investigated fruit samples by headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS), allowed us to distinguish half-red and red strawberries, as the e-nose sensors gave distinct responses to samples with different flavours. Three suitable broad bands were individuated by PCA in the ATR-FTIR spectra to discriminate half-red and red samples: the band centred at 3295 cm⁻¹ is generated by compounds that decline, whereas those at 1717 cm⁻¹ and at 1026 cm⁻¹ stem from compounds that accumulate during ripening. Among the chemical parameters (titratable acidity, total phenols, antioxidant activity and total soluble solid) assayed in this study, only titratable acidity was somehow correlated to ATR-FTIR and IA patterns. Thus, ATR-FTIR spectroscopy and IA might be exploited to rapidly assess titratable acidity, which is an objective indicator of the ripening stage.