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Jin G, Boeschoten S, Hageman J, Zhu Y, Wijffels R, Rinzema A, Xu Y. Identifying Variables Influencing Traditional Food Solid-State Fermentation by Statistical Modeling. Foods 2024; 13:1317. [PMID: 38731688 PMCID: PMC11083392 DOI: 10.3390/foods13091317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/09/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Solid-state fermentation is widely used in traditional food production, but most of the complex processes involved were designed and are carried out without a scientific basis. Often, mathematical models can be established to describe mass and heat transfer with the assistance of chemical engineering tools. However, due to the complex nature of solid-state fermentation, mathematical models alone cannot explain the many dynamic changes that occur during these processes. For example, it is hard to identify the most important variables influencing product yield and quality fluctuations. Here, using solid-state fermentation of Chinese liquor as a case study, we established statistical models to correlate the final liquor yield with available industrial data, including the starting content of starch, water and acid; starting temperature; and substrate temperature profiles throughout the process. Models based on starting concentrations and temperature profiles gave unsatisfactory yield predictions. Although the most obvious factor is the starting month, ambient temperature is unlikely to be the direct driver of differences. A lactic-acid-inhibition model indicates that lactic acid from lactic acid bacteria is likely the reason for the reduction in yield between April and December. Further integrated study strategies are necessary to confirm the most crucial variables from both microbiological and engineering perspectives. Our findings can facilitate better understanding and improvement of complex solid-state fermentations.
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Affiliation(s)
- Guangyuan Jin
- The Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology, Jiangnan University, Wuxi 214122, China;
| | - Sjoerd Boeschoten
- Bioprocess Engineering, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands; (S.B.); (Y.Z.); (R.W.); (A.R.)
| | - Jos Hageman
- Biometris, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands;
| | - Yang Zhu
- Bioprocess Engineering, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands; (S.B.); (Y.Z.); (R.W.); (A.R.)
| | - René Wijffels
- Bioprocess Engineering, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands; (S.B.); (Y.Z.); (R.W.); (A.R.)
| | - Arjen Rinzema
- Bioprocess Engineering, Wageningen University and Research, P.O. Box 16, 6700 AA Wageningen, The Netherlands; (S.B.); (Y.Z.); (R.W.); (A.R.)
| | - Yan Xu
- The Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology, Jiangnan University, Wuxi 214122, China;
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2
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Wu X, He F, Wu B, Zeng S, He C. Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis. Foods 2023; 12:foods12030541. [PMID: 36766070 PMCID: PMC9913903 DOI: 10.3390/foods12030541] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/12/2023] [Accepted: 01/21/2023] [Indexed: 01/28/2023] Open
Abstract
The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade.
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Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Fei He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Shupeng Zeng
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Chengyu He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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3
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Lin Y, Ma J, Wang Q, Sun DW. Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Crit Rev Food Sci Nutr 2022; 63:1649-1669. [PMID: 36222697 DOI: 10.1080/10408398.2022.2131725] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
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.
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Affiliation(s)
- Yuandong Lin
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission, South China University of Technology, Guangzhou 510641, China
| | - Qijun Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China.,Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Centre, South China University of Technology, Guangzhou 510006, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.,Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Dublin 4, Ireland
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4
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Characterization of key sulfur aroma compounds and enantiomer distribution in Yingjia Gongjiu. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Abstract
Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications.
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Liu X, Zhang J, Ma Y, Zhao D, Huo D, Luo H, Li J, Luo X, Hou C. A minimalist fluorescent MOF sensor array for Baijiu identification. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:1585-1593. [PMID: 35364606 DOI: 10.1039/d2ay00166g] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The beverage industry is in the market tuyere, and grasps the lifeline of the social economy, so it is particularly vital to supervise it. Herein, we report a minimalist fluorescence metal-organic framework (MOF) sensor array for Baijiu identification. The original MOF was modified by rare-earth elements to obtain Eu-MOFs and Tb-MOFs. They exhibited multiple fluorescence characteristic peaks in the range of 400-700 nm when excited at 325 nm. The organic molecules in the Baijiu reacted with Eu-MOF and Tb-MOF, affecting the energy transfer of the entire system and the electronic transition of Eu3+ and Tb3+, which were accompanied by various changes in the fluorescence intensity. For each analyte, a unique fluorescence fingerprint and heat map would be formed, which could be discriminated by pattern recognition. With this method, 11 kinds of organic molecules were identified accurately. A good stepwise bilinear function between the fluorescence intensity and concentrations of ferulic acid was obtained at 0.2-3.13 μM and 6.25-100 μM. Crucially, 16 kinds of Baijius of different brands were accurately identified with an accuracy of 100%. The sensor array showed the advantages of strong maneuverability and fast response, exhibiting good application value in Baijiu detection.
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Affiliation(s)
- Xiaofang Liu
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University, Chongqing 400044, PR China.
| | - Jing Zhang
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University, Chongqing 400044, PR China.
| | - Yi Ma
- Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province, College of Bioengineering, Sichuan University of Science and Engineering, Zigong, 643000, PR China
| | - Dong Zhao
- Strong-flavor Baijiu Solid-state Fermentation Key Laboratory of China Light Industry, Wuliangye Group Co., Ltd, Yibin, 644007, PR China
| | - Danqun Huo
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University, Chongqing 400044, PR China.
| | - Huibo Luo
- Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province, College of Bioengineering, Sichuan University of Science and Engineering, Zigong, 643000, PR China
| | - Jiawei Li
- Chongqing University Three Gorges Hospital, Chongqing, 404000, PR China.
| | - Xiaogang Luo
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University, Chongqing 400044, PR China.
- Liquor Making Biology Technology and Application of Key Laboratory of Sichuan Province, College of Bioengineering, Sichuan University of Science and Engineering, Zigong, 643000, PR China
| | - Changjun Hou
- Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University, Chongqing 400044, PR China.
- Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, PR China
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Zhang T, Wu X, Wu B, Dai C, Fu H. Rapid authentication of the geographical origin of milk using portable near‐infrared spectrometer and fuzzy uncorrelated discriminant transformation. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tingfei Zhang
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
- High‐tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
- High‐tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province Jiangsu University Zhenjiang China
| | - Bin Wu
- Department of Information Engineering Chuzhou Polytechnic Chuzhou China
| | - Chunxia Dai
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Haijun Fu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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8
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WANG A, ZHU Y, QIU J, CAO R, ZHU H. Application of intelligent sensory technology in the authentication of alcoholic beverages. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.32622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Affiliation(s)
| | | | - Ju QIU
- China Agricultural University, China
| | - Ruge CAO
- Tianjin University of Science and Technology, China
| | - Hong ZHU
- Ministry of Agriculture and Rural Affairs, China
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9
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Abstract
Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.
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10
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Hu X, Chen P, Tian J, Huang D, Luo H, Huang D. Predicting the moisture content of Daqu with hyperspectral imaging. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2020. [DOI: 10.1515/ijfe-2019-0243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Abstract
Daqu, a Chinese liquor fermentation starter, contains all kinds of microorganisms and enzymes for Chinese liquor fermentation. The moisture content of Daqu significantly influence on the reproduction of microorganisms in Daqu. This work presents for the first time that determination of moisture content of Daqu with hyperspectral imaging. The characteristic spectrum of water is extracted based on comparative experiments with varying moisture content. The molds based on the full bands and feature bands were established by the support vector regression (SVR) method, which is used to predict moisture content of Daqu during fermentation process. The performance of the model based on the feature bands (R
2 = 0.9870, root mean square error (RMSE) = 0.0091) is comparable to the full bands and the dimensions of the spectral information were significantly reduced. This work presents a novel, rapid and nondestructive approach for detecting the moisture content in Daqu and lays a foundation for the application of hyperspectral imaging.
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Affiliation(s)
- Xinjun Hu
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
- Sichuan Provincial Key Laboratory of Process Equipment and Control , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Ping Chen
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Jianping Tian
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Danping Huang
- School of Mechanical Engineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Huibo Luo
- College of Bioengineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
| | - Dan Huang
- College of Bioengineering , Sichuan University of Science and Engineering , Zigong , Sichuan 643000 , China
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11
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Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation. Journal of Food Science and Technology 2019; 57:1310-1319. [PMID: 32180627 DOI: 10.1007/s13197-019-04165-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 10/19/2019] [Accepted: 11/08/2019] [Indexed: 10/25/2022]
Abstract
Due to the difference of raw materials and brewing technology, the quality and flavours of vinegar are different. Different kinds of vinegar have different functions and effects. Therefore, it is important to classify the vinegar varieties correctly. This work presented a new fuzzy feature extraction algorithm, called fuzzy Foley-Sammon transformation (FFST), and designed the electronic nose (E-nose) system for classifying vinegar varieties successfully. Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. FFST, Foley-Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively. Then, K nearest neighbor (KNN) served as a classifier for the classification of vinegar varieties. The highest identification accuracy rate was 96.92% by using the FFST and KNN. Therefore, the E-nose system combined with the FFST was an effective method to identify Chinese vinegar varieties and this method has wide application prospects.
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