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Song P, Huang Y, Li J, Shan S, Zhou Z, Cao H, Zhao C. The influence of processing technologies on the biological activity of carbohydrates in food. Food Chem X 2024; 23:101590. [PMID: 39036475 PMCID: PMC11260335 DOI: 10.1016/j.fochx.2024.101590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 06/09/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024] Open
Abstract
Food processing transforms raw materials into different food forms using physical or chemical techniques. Recently, carbohydrates have gained attention for their diverse biological activities like antioxidant, anticancer, and antimutagenic effects. Selecting suitable processing methods is crucial to preserve the beneficial properties of carbohydrates. This review discusses the impact of non-thermal and thermal processing on the physicochemical and biological traits of carbohydrates, highlighting the need for understanding the mechanisms underlying these changes. Future research will focus on enhancing and safeguarding the biological and functional aspects of carbohydrates through improved processing techniques. The goal is to optimize methods that maintain the beneficial properties of carbohydrates, maximizing their health benefits for consumers.
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Affiliation(s)
- Peng Song
- College of Life Sciences, Liaocheng University, Liaocheng 252000, China
| | - Yajun Huang
- College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Jingru Li
- College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Shuo Shan
- College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Universidade de Vigo, Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Sciences, 32004 Ourense, Spain
| | - Zhengsong Zhou
- College of Life Sciences, Liaocheng University, Liaocheng 252000, China
- Shandong Aocter Biotechnology Co., Ltd, Liaocheng 252000, China
| | - Hui Cao
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Product Processing and Safety, Zhanjiang 524088, China
- Universidade de Vigo, Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Sciences, 32004 Ourense, Spain
| | - Chao Zhao
- College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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Guo H, Cai Y, Ogawa Y, Shiraga K, Kondo N, Ogawa Y. Quantification of resistant starch content in rice after hydrothermal treatments using terahertz spectroscopy. Food Res Int 2024; 186:114400. [PMID: 38729703 DOI: 10.1016/j.foodres.2024.114400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/17/2024] [Accepted: 04/20/2024] [Indexed: 05/12/2024]
Abstract
Since hydrothermal treatments can enhance resistant starch (RS) content in rice and provide health benefits when consumed, a less laborious and non-destructive method to determine RS content is needed. Terahertz (THz) spectroscopy is hypothesized as a suitable method to quantify RS content in rice after hydrothermal treatment with its sensitivity for the intermolecular forces increase in the formation of RS. In this study, we first used the traditional in vitro hydrolysis method to determine the content of RS in rice. Then, the potential of starch absorbance peaks to quantify RS content after three commonly used hydrothermal methods, soaking, mild heat-moisture treatment, and parboiling, was investigated. The second derivative intensities of the peak at 9.0, 10.5, 12.1, and 13.1 THz were confirmed as being correlated with RS content and showed the high accuracy to predict RS content in samples (R2 > 0.96). Our results indicate the RS content of hydrothermally treated rice can be accurately quantified using these peaks.
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Affiliation(s)
- Han Guo
- Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan
| | - Yidi Cai
- College of Food Science and Engineering, Dalian Ocean University, Dalian 116023, China
| | - Yukiharu Ogawa
- Graduate School of Horticulture, Chiba University, 648, Matsudo, Matsudo 271-8501, Japan
| | - Keiichiro Shiraga
- Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan; PRESTO, Japan Science and Technology Agency, Kawaguchi 332-0012, Japan
| | - Naoshi Kondo
- Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan
| | - Yuichi Ogawa
- Graduate School of Agriculture, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan.
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Pu H, Yu J, Luo J, Paliwal J, Sun DW. Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 311:124015. [PMID: 38359515 DOI: 10.1016/j.saa.2024.124015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/31/2024] [Accepted: 02/07/2024] [Indexed: 02/17/2024]
Abstract
Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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
| | - Jingxiao Yu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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
| | - Jie Luo
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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
| | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, 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, Belfield, Dublin 4, Ireland.
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Huang L, Liu M, Li B, Chitrakar B, Duan X. Terahertz Spectroscopic Identification of Roast Degree and Variety of Coffee Beans. Foods 2024; 13:389. [PMID: 38338523 PMCID: PMC10855191 DOI: 10.3390/foods13030389] [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: 11/21/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
In this study, terahertz time-domain spectroscopy (THz-TDS) was proposed to identify coffee of three different varieties and three different roasting degrees of one variety. Principal component analysis (PCA) was applied to extract features from frequency-domain spectral data, and the extracted features were used for classification prediction through linear discrimination (LD), support vector machine (SVM), naive Bayes (NB), and k-nearest neighbors (KNN). The classification effect and misclassification of the model were analyzed via confusion matrix. The coffee varieties, namely Catimor, Typica 1, and Typica 2, under the condition of shallow drying were used for comparative tests. The LD classification model combined with PCA had the best effect of dimension reduction classification, while the speed and accuracy reached 20 ms and 100%, respectively. The LD model was found with the highest speed (25 ms) and accuracy (100%) by comparing the classification results of Typica 1 for three different roasting degrees. The coffee bean quality detection method based on THz-TDS combined with a modeling analysis method had a higher accuracy, faster speed, and simpler operation, and it is expected to become an effective detection method in coffee identification.
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Affiliation(s)
- Luelue Huang
- School of Food and Drug, Shenzhen Polytechnic University, No. 2190, Liuxian Road, Shenzhen 518055, China; (L.H.); (M.L.)
| | - Miaoling Liu
- School of Food and Drug, Shenzhen Polytechnic University, No. 2190, Liuxian Road, Shenzhen 518055, China; (L.H.); (M.L.)
| | - Bin Li
- School of Food and Drug, Shenzhen Polytechnic University, No. 2190, Liuxian Road, Shenzhen 518055, China; (L.H.); (M.L.)
| | - Bimal Chitrakar
- College of Food Science and Technology, Hebei Agricultural University, Baoding 071001, China;
| | - Xu Duan
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471003, China
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