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Zhang M, Zhao B, Li L, Nie L, Li P, Sun J, Wu A, Zang H. A rapid extraction process monitoring of Swertia mussotii Franch. With near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 295:122609. [PMID: 36921517 DOI: 10.1016/j.saa.2023.122609] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/05/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Swertia mussotii Franch. (SMF), a traditional Tibetan medicine, which has miraculous effect on treating hepatitis diseases. However, there is no research on its entire production process, and invisible production process has seriously hindered the SMF modern development. In this study, principal component analysis (PCA), subtractive spectroscopy, and two-dimensional correlation spectroscopy (2D-COS) were used to explain changes of characteristic groups in the extraction process. Four main characteristic peaks at 1884 nm, 1944 nm, 2246 nm and 2308 nm were identified to describe the changes of molecular structure information of total active components in SMF extraction process. In addition, multi critical quality attributes (CQAs) models were established by near-infrared spectroscopy (NIRS) combined with the total quantum statistical moment (TQSM). The coefficients of determination (R2eval and R2ival) were both greater than 0.99. The ratios of the standard deviation of validation to the standard error of the prediction (RPDe and RPDi) were greater than five. The quantitative model of AUCT could save time on primary data measurement by not requiring determination of indicator components compared with others. In conclusion, these results demonstrated that it was feasible to understand the SMF extraction process through AUCT and characteristic groups. These could realize the visual digital characterization and quality stability of the SMF extraction process.
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Affiliation(s)
- Mengqi Zhang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Bing Zhao
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Lian Li
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Lei Nie
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Peipei Li
- Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810008, China
| | - Jing Sun
- Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810008, China
| | - Aoli Wu
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Hengchang Zang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; National Glycoengineering Research Center, Shandong University, Jinan, Shandong, 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, 250012, China.
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2
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Ren Y, Fu Y, Sun DW. Analyzing the effects of nonthermal pretreatments on the quality of microwave vacuum dehydrated beef using terahertz time-domain spectroscopy and near-infrared hyperspectral imaging. Food Chem 2023; 428:136753. [PMID: 37429244 DOI: 10.1016/j.foodchem.2023.136753] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/24/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023]
Abstract
Both nonthermal pretreatment and nondestructive analysis are effective technologies in improving drying processes. This study evaluated the effects of different pretreatment methods on the quality of beef dehydrated by microwave vacuum drying (MVD) and compared the MVD process performance comprising real-time moisture content (MC), MC loss, colour content, and shrinkage rate using different optical sensing methods including terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI). Results indicated that osmotic pretreatment improved the drying rate of MVD beef with lower changes in colour and shrinkage rate. Both THz-TDS-based and NIR-HSI-based on-site direct scanning and in-situ in-direct sensing showed accurate prediction results, with best R2p of 0.9646 and 0.9463 for MC and R2p of 0.9817 and 0.9563 for MC loss prediction, respectively. NIR-HSI visualisation of MC results showed that ultrasound pretreatment curbed but osmotic pretreatment promoted nonuniform distribution during MVD. This research should guide improving the industrial MVD drying process.
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Affiliation(s)
- Yuqiao Ren
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture and Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Ying Fu
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture and Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture and Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland.
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3
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Nurkhoeriyati T, Arefi A, Kulig B, Sturm B, Hensel O. Non-destructive monitoring of quality attributes kinetics during the drying process: A case study of celeriac slices and the model generalisation in selected commodities. Food Chem 2023; 424:136379. [PMID: 37229901 DOI: 10.1016/j.foodchem.2023.136379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
The potential of Visual-NIR hyperspectral imaging (VNIR-HSI, 425-1700 nm) to predict celeriac quality attributes during the drying process was investigated. The HSI-Gaussian Process Regression (GPR) fusion method excellently predicted moisture content (MC, R2 ≈ 1.00, RMSE = 0.77 gw 100 gs-1) and water activity (aw, R2 = 0.98, RMSE = 0.04). Moreover, the rehydration ratio (RR, R2 = 0.89, RMSE = 0.04) and colour indices (R2 = 0.80-0.93, RMSE = 0.17-1.45) were reasonably predicted. However, antioxidant activity (AA) and total phenolic compounds (TPC) were poorly predicted. These results are potentially due to MC variations dominating the NIR region, masking phenolic compounds. Finally, the celeriac-based-trained model was assessed by predicting the MC of apple, cocoyam, and carrot slices. The results were encouraging; however, a GPR model trained on the data of all four commodities was more robust (R2 ≈ 1.00, RMSE = 1-2 gw 100 gs-1).
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Affiliation(s)
- Tina Nurkhoeriyati
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany; Study Program of Food Technology, Faculty of Engineering, International University Liaison Indonesia (IULI), 15310 Tangerang, Indonesia.
| | - Arman Arefi
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469 Potsdam, Germany.
| | - Boris Kulig
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany
| | - Barbara Sturm
- Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469 Potsdam, Germany; Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, 10115 Berlin, Germany
| | - Oliver Hensel
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany
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4
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Taghinezhad E, Szumny A, Figiel A. The Application of Hyperspectral Imaging Technologies for the Prediction and Measurement of the Moisture Content of Various Agricultural Crops during the Drying Process. Molecules 2023; 28:molecules28072930. [PMID: 37049695 PMCID: PMC10096048 DOI: 10.3390/molecules28072930] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 03/29/2023] Open
Abstract
Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.
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Affiliation(s)
- Ebrahim Taghinezhad
- Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
- Department of Food Chemistry and Biocatalysis, Wroclaw University of Environmental and Life Science, CK Norwida 25, 50-375 Wrocław, Poland
- Correspondence:
| | - Antoni Szumny
- Department of Food Chemistry and Biocatalysis, Wroclaw University of Environmental and Life Science, CK Norwida 25, 50-375 Wrocław, Poland
| | - Adam Figiel
- Institute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, Chełmońskiego 37a, 51-630 Wrocław, Poland
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Brandão SCR, da Silva EM, de Arruda GMP, de Souza Netto JM, de Medeiros RAB, Honorato FA, Azoubel PM. Ethanol pretreatment and infrared drying of melon: Kinetics, quality parameters, and
NIR
spectra. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Shirley Clyde Rupert Brandão
- Departamento de Engenharia Química Universidade Federal de Pernambuco Recife PE Brazil
- Department of Food Science and Technology, College of Food, Agricultural, and Environmental Sciences The Ohio State University Columbus OH USA
| | - Elaine Maria da Silva
- Departamento de Engenharia Química Universidade Federal de Pernambuco Recife PE Brazil
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6
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Salehi F. Recent advances in the ultrasound-assisted osmotic dehydration of agricultural products: A review. FOOD BIOSCI 2022. [DOI: 10.1016/j.fbio.2022.102307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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7
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Tian S, Tian H, Yang Q, Xu H. Internal quality assessment of kiwifruit by bulk optical properties and online transmission spectra. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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8
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Xu W, Zhang F, Wang J, Ma Q, Sun J, Tang Y, Wang J, Wang W. Real-Time Monitoring of the Quality Changes in Shrimp (Penaeus vannamei) with Hyperspectral Imaging Technology during Hot Air Drying. Foods 2022. [PMCID: PMC9601712 DOI: 10.3390/foods11203179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Hot air drying is the most common processing method to extend shrimp’s shelf life. Real-time monitoring of moisture content, color, and texture during the drying process is important to ensure product quality. In this study, hyperspectral imaging technology was employed to acquire images of 104 shrimp samples at different drying levels. The water distribution and migration were monitored by low field magnetic resonance and the correlation between water distribution and other quality indicators were determined by Pearson correlation analysis. Then, spectra were extracted and competitive adaptive reweighting sampling was used to optimize characteristic variables. The grey-scale co-occurrence matrix and color moments were used to extract the textural and color information from the images. Subsequently, partial least squares regression and least squares support vector machine (LSSVM) models were established based on full-band spectra, characteristic spectra, image information, and fused information. For moisture, the LSSVM model based on full-band spectra performed the best, with residual predictive deviation (RPD) of 2.814. For L*, a*, b*, hardness, and elasticity, the optimal models were established by LSSVM based on fused information, with RPD of 3.292, 2.753, 3.211, 2.807, and 2.842. The study provided an in situ and real-time alternative to monitor quality changes of dried shrimps.
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Zhao Y, Kang Z, Chen L, Guo Y, Mu Q, Wang S, Zhao B, Feng C. Quality classification of kiwifruit under different storage conditions based on deep learning and hyperspectral imaging technology. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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10
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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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11
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Arefi A, Sturm B, Raut S, von Gersdorff G, Hensel O. NIR laser-based imaging techniques to monitor quality attributes of apple slices during drying process: Laser-light backscattering & biospeckle imaging techniques. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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12
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Malvandi A, Feng H, Kamruzzaman M. Application of NIR spectroscopy and multivariate analysis for Non-destructive evaluation of apple moisture content during ultrasonic drying. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 269:120733. [PMID: 34920303 DOI: 10.1016/j.saa.2021.120733] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/14/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R2p = 0.99 and RMSEP = 3.32%. The results revealed that portable NIRS combined with multivariate analysis is quite promising for monitoring and evaluating the moisture content during ultrasonic drying.
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Affiliation(s)
- Amir Malvandi
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA
| | - Hao Feng
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA; Department of Food Science and Human Nutrition, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA.
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Short- and Medium-Wave Infrared Drying of Cantaloupe (Cucumis melon L.) Slices: Drying Kinetics and Process Parameter Optimization. Processes (Basel) 2022. [DOI: 10.3390/pr10010114] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
The main objective of the present work was to study the drying kinetics and obtain the optimum process parameters of cantaloupe slices using short-and medium-wave infrared radiation (SMIR) drying technology. The effect of three independent variables of infrared radiation temperature (55–65 °C), slice thickness (5–9 mm) and radiation distance (80–160 mm) on the L value, color difference (∆E), hardness and vitamin C content were investigated by using the Response Surface Methodology (RSM). The results showed that the Page model can adequately predict the moisture content between 55 and 65 °C (R2 > 0.99). The effective moisture diffusivity (Deff) varied from 5.26 × 10−10 to 2.09 × 10−9 m2/s and the activation energy (Ea) of the SMIR drying was 31.84 kJ/mol. Infrared radiation temperature and slice thickness exerted extremely significant effects on L value and color difference (ΔE) (p < 0.01), with higher infrared radiation temperature and thin slice thickness leading to a decrease in the L value and an increase in ΔE. Hardness and vitamin C content were significantly affected by infrared radiation temperature, slice thickness and radiation distance, of which the slice thickness was the most distinct factor affecting the hardness value. Higher infrared radiation temperature and larger slice thickness and radiation distance resulted in higher vitamin C degradation. For the given constraints (maximized vitamin C content and L value, minimized ΔE and hardness value), the optimum drying parameters were infrared radiation temperature 58.2 °C, slice thickness 6 mm and radiation distance 90 mm. Under the optimum drying combination conditions, the experimental values were 65.58 (L value), 8.57 (∆E), 10.49 N (hardness) and 106.58 mg/100 g (vitamin C content), respectively. This study is beneficial to the development of the cantaloupe food processing industry and provides more insights for the application of SMIR drying technology to improve the drying rate and product quality of cantaloupe.
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Ren Y, Lin X, Lei T, Sun DW. Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes. Crit Rev Food Sci Nutr 2021; 62:4267-4293. [PMID: 34275402 DOI: 10.1080/10408398.2021.1947773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.
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Affiliation(s)
- Yuqiao Ren
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Xiaohui Lin
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Tong Lei
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
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