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Olakanmi SJ, Bharathi VSK, Jayas DS, Paliwal J. Innovations in nondestructive assessment of baked products: Current trends and future prospects. Compr Rev Food Sci Food Saf 2024; 23:e13385. [PMID: 39031741 DOI: 10.1111/1541-4337.13385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/13/2024] [Accepted: 05/18/2024] [Indexed: 07/22/2024]
Abstract
Rising consumer awareness, coupled with advances in sensor technology, is propelling the food manufacturing industry to innovate and employ tools that ensure the production of safe, nutritious, and environmentally sustainable products. Amidst a plethora of nondestructive techniques available for evaluating the quality attributes of both raw and processed foods, the challenge lies in determining the most fitting solution for diverse products, given that each method possesses its unique strengths and limitations. This comprehensive review focuses on baked goods, wherein we delve into recently published literature on cutting-edge nondestructive methods to assess their feasibility for Industry 4.0 implementation. Emphasizing the need for quality control modalities that align with consumer expectations regarding sensory traits such as texture, flavor, appearance, and nutritional content, the review explores an array of advanced methodologies, including hyperspectral imaging, magnetic resonance imaging, terahertz, acoustics, ultrasound, X-ray systems, and infrared spectroscopy. By elucidating the principles, applications, and impacts of these techniques on the quality of baked goods, the review provides a thorough synthesis of the most current published studies and industry practices. It highlights how these methodologies enable defect detection, nutritional content prediction, texture evaluation, shelf-life forecasting, and real-time monitoring of baking processes. Additionally, the review addresses the inherent challenges these nondestructive techniques face, ranging from cost considerations to calibration, standardization, and the industry's overreliance on big data.
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Affiliation(s)
- Sunday J Olakanmi
- Department of Biosystems Engineering, 75 Chancellors Circle, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Vimala S K Bharathi
- Department of Biosystems Engineering, 75 Chancellors Circle, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Digvir S Jayas
- Department of Biosystems Engineering, 75 Chancellors Circle, University of Manitoba, Winnipeg, Manitoba, Canada
- President's Office, 4401 University Drive West, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Jitendra Paliwal
- Department of Biosystems Engineering, 75 Chancellors Circle, University of Manitoba, Winnipeg, Manitoba, Canada
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Zhou J, Li F, Wang X, Yin H, Zhang W, Du J, Pu H. Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll. PLANTS (BASEL, SWITZERLAND) 2024; 13:1270. [PMID: 38732485 PMCID: PMC11085301 DOI: 10.3390/plants13091270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.
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Affiliation(s)
- Ju Zhou
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Feiyi Li
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Xinwu Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China;
| | - Heng Yin
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Wenjing Zhang
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Jiaoyang Du
- Forge Business School, Chongqing Yitong University, He’chuan 401520, China;
| | - Haibo Pu
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
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Olakanmi SJ, Jayas DS, Paliwal J, Chaudhry MMA, Findlay CRJ. Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging. Foods 2024; 13:231. [PMID: 38254532 PMCID: PMC10814855 DOI: 10.3390/foods13020231] [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: 12/13/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave infrared (SWIR) range)) of fava bean-fortified bread and classify them based on their color characteristics (visible-near-infrared (Vis-NIR) range). Different multivariate analysis tools, such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLSR), were utilized to assess the protein distribution and color quality parameters of bread samples. The result of the PLS-DA in the SWIR range yielded a classification accuracy of ˃99%, successfully classifying the samples based on their protein contents (low protein and high protein). The PLSR model showed an RMSEC of 0.086% and an RMSECV of 0.094%. Also, the external validation resulted in an RMSEP of 0.064%. The PLSR model possessed the capability to efficiently predict the protein content of the bread samples. The results suggest that HSI can be successfully used to classify bread samples based on their protein content and for the prediction of protein composition. Hyperspectral imaging can therefore be reliably implemented for the quality monitoring of baked goods in commercial bakeries.
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Affiliation(s)
- Sunday J. Olakanmi
- Department of Biosystems Engineering, University of Manitoba, 75 Chancellors Circle, Winnipeg, MB R3T 5V6, Canada; (S.J.O.); (M.M.A.C.); (C.R.J.F.)
| | - Digvir S. Jayas
- Department of Biosystems Engineering, University of Manitoba, 75 Chancellors Circle, Winnipeg, MB R3T 5V6, Canada; (S.J.O.); (M.M.A.C.); (C.R.J.F.)
- President’s Office, University of Lethbridge, 4401 University Drive West, Lethbridge, AB T1K 3M4, Canada
| | - Jitendra Paliwal
- Department of Biosystems Engineering, University of Manitoba, 75 Chancellors Circle, Winnipeg, MB R3T 5V6, Canada; (S.J.O.); (M.M.A.C.); (C.R.J.F.)
| | - Muhammad Mudassir Arif Chaudhry
- Department of Biosystems Engineering, University of Manitoba, 75 Chancellors Circle, Winnipeg, MB R3T 5V6, Canada; (S.J.O.); (M.M.A.C.); (C.R.J.F.)
| | - Catherine Rui Jin Findlay
- Department of Biosystems Engineering, University of Manitoba, 75 Chancellors Circle, Winnipeg, MB R3T 5V6, Canada; (S.J.O.); (M.M.A.C.); (C.R.J.F.)
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Tantinantrakun A, Thompson AK, Terdwongworakul A, Teerachaichayut S. Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging. Foods 2023; 12:2793. [PMID: 37509885 PMCID: PMC10379852 DOI: 10.3390/foods12142793] [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: 06/17/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (Rp) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.
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Affiliation(s)
- Achiraya Tantinantrakun
- Department of Food Science, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
| | - Anthony Keith Thompson
- Department of Postharvest Technology, Cranfield University, College Road, Cranfield, Bedford MK430AL, UK
| | - Anupun Terdwongworakul
- Department of Agricultural Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
| | - Sontisuk Teerachaichayut
- Department of Food Process Engineering, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
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Sahachairungrueng W, Thompson AK, Terdwongworakul A, Teerachaichayut S. Non-Destructive Classification of Organic and Conventional Hens' Eggs Using Near-Infrared Hyperspectral Imaging. Foods 2023; 12:2519. [PMID: 37444257 DOI: 10.3390/foods12132519] [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: 06/03/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares-discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.
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Affiliation(s)
- Woranitta Sahachairungrueng
- Department of Food Science, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
| | - Anthony Keith Thompson
- Department of Postharvest Technology, Cranfield University, College Road, Cranfield, Bedford MK43 0AL, UK
| | - Anupun Terdwongworakul
- Department of Agricultural Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen, Nakhon Pathom 73140, Thailand
| | - Sontisuk Teerachaichayut
- Department of Food Process Engineering, School of Food-Industry, King Mongkut's Institute of Technology Ladkrabang, Chalongkrung Road, Ladkrabang, Bangkok 10520, Thailand
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Jin S, Liu X, Wang J, Pan L, Zhang Y, Zhou G, Tang C. Hyperspectral imaging combined with fluorescence for the prediction of microbial growth in chicken breasts under different packaging conditions. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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Liu Y, Dixit Y, Reis MM, Prabakar S. Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122220. [PMID: 36516590 DOI: 10.1016/j.saa.2022.122220] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/27/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a Rcv2 in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.
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Affiliation(s)
- Yang Liu
- Leather and Shoe Research Association of New Zealand, PO Box 8094, Hokowhitu, Palmerston North 4446, New Zealand.
| | - Yash Dixit
- Food Informatics, Smart Foods, AgResearch Ltd, Te Ohu Rangahau Kai, Massey University, Palmerston North, New Zealand.
| | - Marlon M Reis
- Food Informatics, Smart Foods, AgResearch Ltd, Te Ohu Rangahau Kai, Massey University, Palmerston North, New Zealand.
| | - Sujay Prabakar
- Leather and Shoe Research Association of New Zealand, PO Box 8094, Hokowhitu, Palmerston North 4446, New Zealand.
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Olakanmi SJ, Jayas DS, Paliwal J. Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review. Compr Rev Food Sci Food Saf 2023; 22:1817-1838. [PMID: 36916025 DOI: 10.1111/1541-4337.13131] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 01/10/2023] [Accepted: 02/13/2023] [Indexed: 03/16/2023]
Abstract
One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods. These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.
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Affiliation(s)
- Sunday J Olakanmi
- Department of Biosystems Engineering, 75 Chancellors Circle, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada
| | - Digvir S Jayas
- Department of Biosystems Engineering, 75 Chancellors Circle, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada
| | - Jitendra Paliwal
- Department of Biosystems Engineering, 75 Chancellors Circle, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada
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Storage of wafer cookies: Assessment by destructive techniques, and non-destructive spectral detection methods. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111209] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Rapid resolution of types and proportions of broken grains using hyperspectral imaging and optimization algorithm. J Cereal Sci 2022. [DOI: 10.1016/j.jcs.2022.103565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Abstract
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.
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Jang KE, Kim G, Shin MH, Cho JG, Jeong JH, Lee SK, Kang D, Kim JG. Field Application of a Vis/NIR Hyperspectral Imaging System for Nondestructive Evaluation of Physicochemical Properties in ‘Madoka’ Peaches. PLANTS 2022; 11:plants11172327. [PMID: 36079708 PMCID: PMC9460469 DOI: 10.3390/plants11172327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022]
Abstract
Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R2) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R2 values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R2 = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.
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Affiliation(s)
- Kyeong Eun Jang
- Division of Applied Life Science, Graduate School of Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Geonwoo Kim
- Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Mi Hee Shin
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Jung Gun Cho
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Jae Hoon Jeong
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Seul Ki Lee
- Fruit Research Division, National Institute of Horticultural and Herbal Science, Wanju 55365, Korea
| | - Dongyoung Kang
- Department of Bio-industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
| | - Jin Gook Kim
- Institute of Agriculture and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Department of Horticulture, College of Agriculture and Life Science, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, Korea
- Correspondence:
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Zhou L, Wang X, Zhang C, Zhao N, Taha MF, He Y, Qiu Z. Powdery Food Identification Using NIR Spectroscopy and Extensible Deep Learning Model. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02866-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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14
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Du Z, Tian W, Tilley M, Wang D, Zhang G, Li Y. Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review. Compr Rev Food Sci Food Saf 2022; 21:2956-3009. [PMID: 35478437 DOI: 10.1111/1541-4337.12958] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 01/15/2023]
Abstract
Wheat is one of the most widely cultivated crops throughout the world. A great need exists for wheat quality assessment for breeding, processing, and products production purposes. Near-infrared spectroscopy (NIRS) is a rapid, low-cost, simple, and nondestructive assessment method. Many advanced studies associated with NIRS for wheat quality assessment have been published recently, either introducing new chemometrics or attempting new assessment parameters to improve model robustness and accuracy. This review provides a comprehensive overview of NIRS methodology including its principle, spectra pretreatments, spectral wavelength selection, outlier disposal, dataset division, regression methods, and model evaluation. More importantly, the applications of NIRS in the determination of analytical parameters, rheological parameters, and end product quality of wheat are summarized. Although NIRS showed great potential in the quantitative determination of analytical parameters, there are still challenges in model robustness and accuracy in determining rheological parameters and end product quality for wheat products. Future model development needs to incorporate larger databases, integrate different spectroscopic techniques, and introduce cutting-edge chemometrics methods. In addition, calibration based on external factors should be considered to improve the predicted results of the model. The NIRS application in micronutrients needs to be extended. Last, the idea of combining standard product sensory attributes and spectra for model development deserves further study.
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Affiliation(s)
- Zhenjiao Du
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
| | - Wenfei Tian
- National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Michael Tilley
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, Manhattan, Kansas, USA
| | - Donghai Wang
- Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, Kansas, USA
| | - Guorong Zhang
- Agricultural Research Center-Hays, Kansas State University, Hays, Kansas, USA
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas, USA
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15
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Panda BK, Mishra G, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2021.110889] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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16
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Priyashantha H, Höjer A, Saedén KH, Lundh Å, Johansson M, Bernes G, Geladi P, Hetta M. Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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17
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Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104110] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Combination of spectral and image information from hyperspectral imaging for the prediction and visualization of the total volatile basic nitrogen content in cooked beef. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-00983-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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