Trends in Food Science & Technology最新文献_第7页

From insoluble to soluble: An overview of processing methods for dietary fibers in okara

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2024.104861

Robert Spiess, Stephanie Jeske, Mario Arcari, Christoph Denkel

Background:

Dietary fibers are commonly categorized as water-soluble (SDF) or water-insoluble (IDF) dietary fibers. SDF possesses more favorable techno-functional and textural features than IDF. Consequently, many different modification procedures have been suggested to increase the SDF content in various matrices, such as okara, an IDF-rich side stream from soy milk production.

Scope and Approach:

The present review summarizes the treatments reported until August 2024 to increase SDF in okara and systematically compares their efficiencies.

Key Findings and Conclusions:

Biological treatments resulted in the lowest increase in SDF followed by chemical treatments. Physical treatment techniques were very diverse, and some were found to be extremely efficient. They increased the SDF content five- to nine-fold, with size reduction being the most efficient treatment. Selected combinations of techniques boosted the SDF content even more. Most efficient were steam explosion (a subcategory of size reduction treatments) alone or in combination with enzymatic treatment, which increased SDF from 3.5 to 31.9% DM on average, and the alkaline treatment of extruded okara, which increased SDF from 2.8 to 34.1% DM.

Although current treatments achieve high final SDF amounts, one-third of the measured fiber content remains insoluble. This limitation could be addressed by a sound understanding of the structure of the SDF-containing composites and of the dietary fibers themselves, which could enable the design of treatments optimized for higher SDF contents. Higher SDF contents can improve the techno-functional and nutritional properties, as well as the economic value of okara.

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引用次数: 0

Food recommendation towards personalized wellbeing

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2025.104877

Guanhua Qiao , Dachuan Zhang , Nana Zhang , Xiaotao Shen , Xidong Jiao , Wenwei Lu , Daming Fan , Jianxin Zhao , Hao Zhang , Wei Chen , Jinlin Zhu

Background

The intersection of nutrition and technology gave birth to the research of food recommendation system (FRS), which marked the transformation of traditional diet to a more personalized and healthy direction. The FRS uses advanced data analysis and machine learning technology to provide customized dietary advice according to users' personal preferences, and nutritional needs, which plays a vital role in promoting public health and reducing disease risks.

Scope and approach

This review presents the architecture of FRS and deeply discusses various recommendation algorithms, including the content-based method, collaborative filtering method, knowledge graph-based method, and hybrid methods. The review further introduces existing data resources and evaluation metrics, and highlights key technologies in user profiling and food analysis. In addition, the wide application of personalized FRS is summarized, and the importance of these systems in satisfying users' dietary preferences and maintaining balanced nutrition is emphasized. Finally, the key challenges and development trends of FRS are deeply analyzed from data level, model level and user experience level.

Key findings and conclusions

Personalized FRS shows great potential in helping users make healthier dietary decisions. Although there are still many challenges, such as dealing with heterogeneous data and interpretability. But with the progress of technology, there will be broader development in the future. For example, the powerful data processing ability of deep learning will effectively improve the accuracy of the system. In addition, the application of interactive recommendation system and large language model will also provide strong support for satisfying user experience and improving acceptance.

{"title":"Food recommendation towards personalized wellbeing","authors":"Guanhua Qiao , Dachuan Zhang , Nana Zhang , Xiaotao Shen , Xidong Jiao , Wenwei Lu , Daming Fan , Jianxin Zhao , Hao Zhang , Wei Chen , Jinlin Zhu","doi":"10.1016/j.tifs.2025.104877","DOIUrl":"10.1016/j.tifs.2025.104877","url":null,"abstract":"<div><h3>Background</h3><div>The intersection of nutrition and technology gave birth to the research of food recommendation system (FRS), which marked the transformation of traditional diet to a more personalized and healthy direction. The FRS uses advanced data analysis and machine learning technology to provide customized dietary advice according to users' personal preferences, and nutritional needs, which plays a vital role in promoting public health and reducing disease risks.</div></div><div><h3>Scope and approach</h3><div>This review presents the architecture of FRS and deeply discusses various recommendation algorithms, including the content-based method, collaborative filtering method, knowledge graph-based method, and hybrid methods. The review further introduces existing data resources and evaluation metrics, and highlights key technologies in user profiling and food analysis. In addition, the wide application of personalized FRS is summarized, and the importance of these systems in satisfying users' dietary preferences and maintaining balanced nutrition is emphasized. Finally, the key challenges and development trends of FRS are deeply analyzed from data level, model level and user experience level.</div></div><div><h3>Key findings and conclusions</h3><div>Personalized FRS shows great potential in helping users make healthier dietary decisions. Although there are still many challenges, such as dealing with heterogeneous data and interpretability. But with the progress of technology, there will be broader development in the future. For example, the powerful data processing ability of deep learning will effectively improve the accuracy of the system. In addition, the application of interactive recommendation system and large language model will also provide strong support for satisfying user experience and improving acceptance.</div></div>","PeriodicalId":441,"journal":{"name":"Trends in Food Science & Technology","volume":"156 ","pages":"Article 104877"},"PeriodicalIF":15.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DNA aptamer-crosslinked hydrogel sensor: Design, mechanism and application for food safety analysis

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2024.104846

Xinning Wang , Liuqin He , Xiaoxiao Rong , Ling Liu , Yulong Yin , Xiaoying Zhao , Yunxuan Weng

Development of efficient detection strategies for food contaminants is essential for ensuring food safety and preventing foodborne disease. Despite their effectiveness, prevailing approaches face limitations such as complex instrumentation, prolonged process, high cost, and low sensitivity. DNA aptamers are promising novel recognition elements for food contaminants due to their high affinity and specificity for target substance. They can crosslink DNA-modified polymers through complementary base pairing to develop hydrogel sensors. The specific aptamer-target binding competes with aptamer-polymer crosslinking. This competition leads to hydrogel breakdown and a gel-sol transition, generating physical, chemical, electrical or other signals for target analysis. DNA aptamer-crosslinked hydrogel sensors offer significant advantages over traditional sensing techniques, including high accuracy, sensitivity, selectivity, rapid response and low cost. These attributes make them increasingly valuable for food safety analysis, especially for on-site and real-time monitoring. This review provides an insightful overview of the most recent advancements in DNA aptamer-crosslinked hydrogel sensors, focusing on their design, mechanism, applications in food safety analysis, important challenges and future research directions.

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引用次数: 0

Ant lipids: A comprehensive review of their major composition, health benefits, and potential as a future food

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2025.104895

Jishu Raj Chetia, Saranya S, Saurabh Sahu, Dibyakanta Seth

Background

Ant lipids are a novel nutrient-rich dietary lipid source with some unique composition as well as health benefit potentials. This is because they are composed of a relatively balanced mixture of saturated, monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA), phospholipids, sterols, as well as different bioactive compounds, providing diverse nutrition scope.

Scope and approach

This review examines ant lipid composition, extraction techniques, health benefits, and potential applications as food, nutraceuticals, and superfoods, highlighting their viability as sustainable lipid sources for future nutrition.

Key finding and conclusions

Ant lipids are a promising and sustainable food source with special nutritional benefits and bioactive properties. This sustainable source has unique composition, that can help with health applications, advanced biomedical uses, and improve cholesterol. The richness of ant lipids in bioactive properties requires to drive advancements in extraction methods and research on their bioavailability. Ant lipids may help address food security, environmental sustainability, and the demand for functional foods, making them crucial for future food systems.

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引用次数: 0

Cracking Spirulina flavor: Compounds, sensory evaluations, and solutions

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2024.104847

Renata Nolasco Braga-Souto , Monize Bürck , Monica Masako Nakamoto , Anna Rafaela Cavalcante Braga

Background

Cyanobacterium such as Spirulina (Limnospira) afford a high protein alternative when incorporated into food products over natural resources through sustainable aqueous cultivation. While Spirulina offers improvements in texture, nutritional quality, and biological effects, its flavor is a critical challenge that directly affects consumer acceptance.

Scope and approach

Thus, this work aimed to review the scientific literature regarding the volatile compounds and amino acids responsible for Spirulina flavor, taste, and odor and to discuss forthcoming methodologies such as enzymatic or acid hydrolysis, fermentation, microencapsulation and blending with essential oils for modifying its taste profile, for instance, based on experimental design. Additionally, the study reviews current Spirulina-enriched foods to understand consumer perceptions and identify opportunities for advancing innovative products that achieve higher consumer acceptance and buying intention.

Key findings and conclusions

Different technologies for modifying the characteristic Spirulina flavor have shown promising results by reducing compounds responsible for unpleasant odors and generating bioaromas and umami compounds. However, more studies are needed on applying treated Spirulina in food matrices for consumer sensory evaluation. Bakery products and pasta stood out in terms of acceptability when added to Spirulina. At the same time, sensory studies of foods need to adopt standardized protocols, recruit a larger number of participants in acceptance studies, and eliminate biases by ensuring that the researchers responsible for the study do not participate in the sensory evaluations. Furthermore, one aspect requiring investigation is whether health claims on Spirulina food labels can positively influence consumer perception despite its characteristic flavor.

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引用次数: 0

Advances of nanobody-based immunosensors for detecting food contaminants

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2025.104871

Shipeng Gao , Wenhao Yang , Xueyun Zheng , Tianxing Wang , Di Zhang , Xiaobo Zou

Background

Immunoassays are reliable and convenient approaches to realizing the sensitive detection of ultralow levels of analytes (contaminants, allergens, biotoxins, etc.). Antibodies are the core element of constructed immunoassays in assuring their specificity and sensitivity toward target-of-interest. However, conventional antibodies are challenged by instability against environmental variables and economic burdens during their large-scale production. Alternatively, nanobodies are emerging as substitutable biorecognition elements in realizing the detection of food contaminants, exhibiting superior specificity and sensitivity.

Scope and approach

This review provides an up-to-date introduction to nanobody-based immunosensors for sensitively detecting the different contaminants in food matrices. The existing nanobodies that have been screened and employed in the establishment of immunoassays, along with the genetic modification strategies for achieving higher affinity and sensitivity, are summarized. Furthermore, the multifunctionalities of nanobodies in the immunoassays, namely analyte catcher, helper for signal tracer, alternator for surrogate antigen, and selector for mimotope, are introduced. Additionally, the applications of nanobody-derived immunosensors in detecting mycotoxins, pesticides, biotoxins, and allergens are highlighted. The insights to address the current challenges that hamper the broader application of nanobodies in immunosensing applications are provided.

Key findings and conclusions

Nanobodies are promising alternatives to conventional antibodies in terms of stability, flexible genetic engineering, solubility, cost-effectiveness, etc. The ongoing effects of tuning the sequence composition and configuration of novel nanobodies, controlling the spatial arrangement of nanobodies following their conjugation on the scaffolds, and designing portable devices to realize the on-site detection are expected to pave the way for nanobody-based immunosensors in the detection of various food contaminants.

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引用次数: 0

Systematic review of protein quality and n-6 and n-3 lipid profile of alternative ingredients for ready-to-use therapeutic foods (RUTF)

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2025.104883

Sarah Pletts , Maria Alejandra Latorre Prieto , Durga Khatiwada , Alison Fleet , Gerard Bryan Gonzales

Background

Ready-to-Use Therapeutic Food (RUTF) is a lipid-based food used for the treatment of children with severe wasting. Due to its current high cost, formulations using alternative ingredients have been proposed. However, guidance on alternative ingredients for RUTF, especially to meet Codex regulations, is missing in the literature. Hence, this paper aims to provide a guide on the nutritional value of alternative RUTF ingredients, focusing on protein quality and n-6/n-3 fatty acid ratio.

Scope and approach this study used three approaches

First, we systematically reviewed the literature and public tenders from the United Nations Children's Fund on new RUTF formulations. Then, a compilation of alternative ingredients used in these formulations were made and analysed.

Second, we estimated the protein quality – using the protein digestibility-corrected amino acid score (PDCAAS) – and n-6 and n-3 fatty acid profiles of the alternative ingredients using national databases and data from the literature. The content of typical antinutritional factors in these ingredients were also reported.

Third, we estimated the PDCAAS, protein content from dairy sources, added sugar content, and n-6/n-3 fatty acid ratio of sample RUTF formulations using alternative ingredients.

Key findings and conclusions

Using alternative ingredients in RUTF have varying effects on the PDCAAS and fatty acid profile of the final RUTF product. The data presented in this paper will help guide manufacturers and developers of modified RUTF formulations on the use of alternative ingredients and their effect on the nutritional properties of the resulting RUTF product to comply with the 2022 Codex guidelines.

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引用次数: 0

Integrating AI and advanced spectroscopic techniques for precision food safety and quality control

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2024.104850

Imane Ziani , Hamza Bouakline , Abdelqader El Guerraf , Ali El Bachiri , Marie-Laure Fauconnier , Farooq Sher

Traditional methods like high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) are widely used in food analysis but often face limitations in detecting trace contaminants at ultra-low levels or in complex matrices. This review highlights recent breakthroughs in food analysis technologies that deliver unprecedented sensitivity and accuracy for consumers' health protection. Among these advances, Wide Line Surface-Enhanced Raman scattering (WL-SERS) has delivered a tenfold increase in sensitivity, enabling the detection of contaminants like melamine in raw milk at concentrations far below conventional thresholds. Mass spectrometry imaging (MSI), particularly matrix-assisted laser desorption/ionization (MALDI-MSI), has made significant progress in spatial resolution, allowing for precise mapping of food constituents and contaminants. Additionally, two-dimensional liquid chromatography (2D-LC) and multidimensional gas chromatography have evolved rapidly, achieving detection as low as 1 ppb in complex food systems. Innovative sensor technologies, such as the Dpyt near-infrared (NIR) fluorescent probe and electrochemiluminescence (ECL) aptasensors, offer rapid and highly sensitive detection, effectively complementing traditional methods. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) has revolutionized food quality assessment, with models like convolutional neural networks (CNNs) reaching up to 99.85% accuracy in identifying adulterants. Despite these advancements, challenges such as high operational costs, sensor stability and AI's computational demands remain. This review highlights the integration of advanced spectroscopy, AI-driven analysis, and novel sensor technologies, outlining future strategies such as miniaturization, nanomaterial innovations, and standardized protocols. These approaches present transformative pathways for improving the precision, efficiency, and accessibility of food safety and quality management, ultimately enhancing public health protection.

{"title":"Integrating AI and advanced spectroscopic techniques for precision food safety and quality control","authors":"Imane Ziani , Hamza Bouakline , Abdelqader El Guerraf , Ali El Bachiri , Marie-Laure Fauconnier , Farooq Sher","doi":"10.1016/j.tifs.2024.104850","DOIUrl":"10.1016/j.tifs.2024.104850","url":null,"abstract":"<div><div>Traditional methods like high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) are widely used in food analysis but often face limitations in detecting trace contaminants at ultra-low levels or in complex matrices. This review highlights recent breakthroughs in food analysis technologies that deliver unprecedented sensitivity and accuracy for consumers' health protection. Among these advances, Wide Line Surface-Enhanced Raman scattering (WL-SERS) has delivered a tenfold increase in sensitivity, enabling the detection of contaminants like melamine in raw milk at concentrations far below conventional thresholds. Mass spectrometry imaging (MSI), particularly matrix-assisted laser desorption/ionization (MALDI-MSI), has made significant progress in spatial resolution, allowing for precise mapping of food constituents and contaminants. Additionally, two-dimensional liquid chromatography (2D-LC) and multidimensional gas chromatography have evolved rapidly, achieving detection as low as 1 ppb in complex food systems. Innovative sensor technologies, such as the Dpyt near-infrared (NIR) fluorescent probe and electrochemiluminescence (ECL) aptasensors, offer rapid and highly sensitive detection, effectively complementing traditional methods. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) has revolutionized food quality assessment, with models like convolutional neural networks (CNNs) reaching up to 99.85% accuracy in identifying adulterants. Despite these advancements, challenges such as high operational costs, sensor stability and AI's computational demands remain. This review highlights the integration of advanced spectroscopy, AI-driven analysis, and novel sensor technologies, outlining future strategies such as miniaturization, nanomaterial innovations, and standardized protocols. These approaches present transformative pathways for improving the precision, efficiency, and accessibility of food safety and quality management, ultimately enhancing public health protection.</div></div>","PeriodicalId":441,"journal":{"name":"Trends in Food Science & Technology","volume":"156 ","pages":"Article 104850"},"PeriodicalIF":15.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mushrooms in innovative food products: Challenges and potential opportunities as meat substitutes, snacks and functional beverages

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2025.104868

Alex Graça Contato , Carlos Adam Conte-Junior

Background

The rising consumer demand for healthier, sustainable, and plant-based food options has driven significant innovation within the food industry. Mushrooms, known for their rich nutritional content and functional properties, have emerged as a promising ingredient in developing novel food products that align with these consumer preferences.

Scope and approach

This study explores the potential of mushrooms as an alternative and strategic component in food innovation. It examines their nutritional profile, including vitamins, minerals, bioactive compounds, and their functional properties, such as umami flavor and meaty texture. The research also delves into the environmental advantages of mushroom cultivation compared to traditional animal-based food production, highlighting the sustainability benefits. The focus is on applying mushrooms in several food categories, including meat substitutes, snacks, and functional beverages.

Findings and conclusion

Mushrooms are well-suited to address current consumer trends due to their versatility, nutritional benefits, and low environmental impact. They offer a viable alternative to animal-based ingredients, contributing to developing health-oriented, no meat-based products. The study concludes that mushrooms will play a pivotal role in the future of food innovation, driven by ongoing research and technological advancements. As consumers increasingly seek functional foods that promote overall well-being, mushrooms are positioned to become a cornerstone ingredient in creating sustainable, nutritious, and appealing food products.

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引用次数: 0

Deep learning-assisted fluorescence spectroscopy for food quality and safety analysis

IF 15.1 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Trends in Food Science & Technology

Pub Date : 2025-02-01 DOI: 10.1016/j.tifs.2024.104821

Yuan Yuan , Zengtao Ji , Yanwei Fan , Qian Xu , Ce Shi , Jian Lyu , Per Ertbjerg

Background

Fluorescence spectroscopy has been widely employed in the quality assessment of food and agricultural products due to its rapid and accurate measurement characteristics. The large amount of fluorescence data or images generated by fluorescence spectroscopy requires more efficient chemometric methods to process and analyze them. However, conventional machine learning models struggle to achieve high-precision predictions when analyzing high-dimensional fluorescence data samples. Deep learning algorithms exhibit powerful automatic learning capabilities in feature extraction and regression modeling of fluorescence spectra.

Scope and approach

The complex, abstract and high-dimensional features of fluorescence spectroscopy are firstly demonstrated through the characterization of fluorescent substances in food products. Secondly, this paper highlights various challenges confronting the fluorescence spectrum analysis process and summarizes several deep learning algorithms that can address these solutions, including the convolutional neural network (CNN), long and short-term memory network (LSTM), and auto encoder (AE). Additionally, the application of deep learning models based on fluorescent data in food detection is reviewed in this article according to different testing objectives, including food safety inspections, food quality assessment, adulteration identification, and variety identification. The review also focuses on the future development trend of this technique in food quality and safety detection.

Key findings and conclusions

Deep learning approaches combined with fluorescence spectroscopy exhibits immense potential in food quality detection and food discrimination classification. The selections of representative input parameters, suitable preprocessing methods and optimization methods can effectively tackle the problems of lack of samples and model over-fitting. Owing to the rapid advancement of artificial intelligence, the deep learning-based fluorescence spectroscopy technology is poised to evolve towards high precision, high throughput, automation and cost-effectiveness.

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引用次数: 0