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Zhang Z, Ding Y, Hu F, Liu Z, Lin X, Fu J, Zhang Q, Zhang ZH, Ma H, Gao X. Constructing in-situ and real-time monitoring methods during soy sauce production by miniature fiber NIR spectrometers. Food Chem 2024; 460:140788. [PMID: 39126954 DOI: 10.1016/j.foodchem.2024.140788] [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: 05/17/2024] [Revised: 07/24/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024]
Abstract
The digestion rate of steamed soybean (DRSS), protease activity of koji (PAK) and formaldehyde nitrogen content of moromi (FNCM) are key indicators to monitor soy sauce production. Currently, monitoring these indicators relies on workers' experience, which can sometimes lead to low material utilization rates and even fermentation failures. Near-infrared spectra were collected during soybean steaming, as well as koji and moromi fermentation, using miniature fiber spectrometers. These spectra were optimized using four pretreatment methods, and regression models were constructed using PLS, iPLS, and Si-PLS. The evaluation of models in prediction sets was based on the correlation coefficient (Rp) and root mean square error (RMSEP). Results indicated that Rp = 0.9327, RMSEP = 4.37% for DRSS, Rp = 0.9364, RMSEP = 228 U/g for PAK, and Rp = 0.9237, RMSEP =0.148 g/100 mL for FNCM were obtained. The above results coupling with validation experiments demonstrated that the developed in-situ and real-time spectroscopy system could ensure high-quality soy sauce production.
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Affiliation(s)
- Zhankai Zhang
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China
| | - Yanhua Ding
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China
| | - Feng Hu
- Guangdong Meiweixian Flavoring Foods Co., Ltd., 1 Chubang Road, Zhongshan 528437, China
| | - Zhan Liu
- Guangdong Meiweixian Flavoring Foods Co., Ltd., 1 Chubang Road, Zhongshan 528437, China
| | - Xiaodong Lin
- Guangdong Meiweixian Flavoring Foods Co., Ltd., 1 Chubang Road, Zhongshan 528437, China
| | - Jiangyan Fu
- Guangdong Meiweixian Flavoring Foods Co., Ltd., 1 Chubang Road, Zhongshan 528437, China
| | - Qingyu Zhang
- Guangdong Meiweixian Flavoring Foods Co., Ltd., 1 Chubang Road, Zhongshan 528437, China
| | - Zhi-Hong Zhang
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China
| | - Haile Ma
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China.
| | - Xianli Gao
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China.
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Yang X, Arroyo Cerezo A, Berzaghi P, Magrin L. Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 316:124287. [PMID: 38701573 DOI: 10.1016/j.saa.2024.124287] [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: 12/06/2023] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 05/05/2024]
Abstract
The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis. The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60-70%. Conversely, this reduction in accuracy was relatively contained (10-15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts. The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.
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Affiliation(s)
- Xueping Yang
- College of Grassland Science and Technology, China Agricultural University, 100193 Beijing, China; Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy.
| | - Alejandra Arroyo Cerezo
- Department of Analytical Chemistry, University of Granada, C/ Fuentenueva s/n, 18071 Granada, Spain
| | - Paolo Berzaghi
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy; GraiNit s.r.l., 35020 Padova, Italy.
| | - Luisa Magrin
- Department of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy
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Marín-Méndez JJ, Luri Esplandiú P, Alonso-Santamaría M, Remirez-Moreno B, Urtasun Del Castillo L, Echavarri Dublán J, Almiron-Roig E, Sáiz-Abajo MJ. Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food. Curr Res Food Sci 2024; 9:100799. [PMID: 39040225 PMCID: PMC11261282 DOI: 10.1016/j.crfs.2024.100799] [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: 03/08/2024] [Revised: 05/07/2024] [Accepted: 06/23/2024] [Indexed: 07/24/2024] Open
Abstract
Knowledge of the energy and macronutrient content of complex foods is essential for the food industry and to implement population-based dietary guidelines. However, conventional methodologies are time-consuming, require the use of chemical products and the sample cannot be recovered. We hypothesize that the nutritional value of heterogeneous food products can be readily measured instead by using hyperspectral imaging systems (NIR and VIS-NIR) combined with mathematical models previously fitted with spectral profiles.118 samples from different food products were collected for building the predictive models using their hyperspectral imaging data as predictors and their nutritional values as dependent variables. Ten different models were screened (Multivariate Linear regression, Lasso regression, Rigde regression, Elastic Net regression, K-Neighbors regression, Decision trees regression, Partial Least Square, Support Vector Machines, Gradient Boosting regression and Random Forest regression). The best results were obtained with Ridge regression for all parameters. The best performance was for estimating the protein content with a RMSE of 1.02 and a R2 equal to 0.88 in a test set, following by moisture (RMSE of 2.21 and R2 equal to 0.85), energy value (RMSE of 21.84 and R2 equal to 0.76) and total fat (RMSE of 2.17 and R2 equal to 0.72). The performance with carbohydrates (RMSE of 2.12 and R2 equal to 0.61) and ashes (RMSE of 0.25 and R2 equal to 0.38) was worse. This study shows that it is possible to predict the energy and nutrient values of processed complex foods, using hyperspectral imaging systems combined with supervised machine learning methods.
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Affiliation(s)
- Juan-Jesús Marín-Méndez
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Paula Luri Esplandiú
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Miriam Alonso-Santamaría
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Berta Remirez-Moreno
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Leyre Urtasun Del Castillo
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Jaione Echavarri Dublán
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
| | - Eva Almiron-Roig
- Centre for Nutrition Research, University of Navarra. Pamplona, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - María-José Sáiz-Abajo
- National Centre for Food Technology and Safety (CNTA), Crta.NA 134-km 53, 31570, San Adrian, Navarra, Spain
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Xu Y, Chen T, Zhang H, Nuermaimaiti Y, Zhang S, Wang F, Xiao J, Liu S, Shao W, Cao Z, Wang J, Chen Y. Application of Near-Infrared Reflectance Spectroscopy for Predicting Chemical Composition of Feces in Holstein Dairy Cows and Calves. Animals (Basel) 2023; 14:52. [PMID: 38200783 PMCID: PMC10778093 DOI: 10.3390/ani14010052] [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: 11/15/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves.
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Affiliation(s)
- Yiming Xu
- College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China; (Y.X.); (S.Z.); (F.W.); (W.S.)
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Tianyu Chen
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Hongxing Zhang
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Yiliyaer Nuermaimaiti
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Siyuan Zhang
- College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China; (Y.X.); (S.Z.); (F.W.); (W.S.)
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Fei Wang
- College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China; (Y.X.); (S.Z.); (F.W.); (W.S.)
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Jianxin Xiao
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Shuai Liu
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Wei Shao
- College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China; (Y.X.); (S.Z.); (F.W.); (W.S.)
| | - Zhijun Cao
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Jingjun Wang
- State Key Laboratory of Animal Nutrition and Feeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.C.); (H.Z.); (Y.N.); (J.X.); (S.L.); (Z.C.)
| | - Yong Chen
- College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China; (Y.X.); (S.Z.); (F.W.); (W.S.)
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Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse. Meat Sci 2023; 200:109169. [PMID: 37001445 DOI: 10.1016/j.meatsci.2023.109169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 03/14/2023] [Accepted: 03/16/2023] [Indexed: 03/22/2023]
Abstract
Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R2 in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.
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Mantena U, Roy S, Datla R. Evaluation of a digital micro-mirror device based near-infrared spectrometer for rapid and accurate prediction of quality attributes in poultry feed. NFS JOURNAL 2022. [DOI: 10.1016/j.nfs.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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7
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Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104343] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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8
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Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo. REMOTE SENSING 2022. [DOI: 10.3390/rs14061302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
This study develops Near-Infrared Spectroscopy (NIRS) and Mode-Cloning (MC) for the rapid assessment of the nutritional quality of bamboo leaves, the primary diet of giant pandas (Ailuropoda melanoleuca) and red pandas (Ailurus fulgens). To test the NIR-MC approach, we evaluated three species of bamboo (Phyllostachys bissetii, Phyllostachys rubromarginata, Phyllostachys aureosulcata). Mode-Cloning incorporated a Slope and Bias Correction (SBC) transform to crude protein prediction models built with NIR spectra taken from Fine–Ground leaves (master mode). The modified models were then applied to spectra from leaves in the satellite minimal processing modes (Course–Ground, Dry–Whole, and Fresh–Whole). The NIR-MC using the SBC yielded a residual prediction deviation (RPD) = 2.73 and 1.84 for Course–Ground and Dry–Whole sample modes, respectively, indicating a good quantitative prediction of crude protein for minimally processed samples that could be easily acquired under field conditions using a portable drier and grinder. The NIR-MC approach also improved the model of crude protein for spectra collected from Fresh–Whole bamboo leaves in the field. Thus, NIR-MC has the potential to provide a real-time prediction of the macronutrient distribution in bamboo in situ, which affects the foraging behavior and dispersion of giant and red pandas in their natural habitats.
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Buonaiuto G, Cavallini D, Mammi LME, Ghiaccio F, Palmonari A, Formigoni A, Visentin G. The accuracy of NIRS in predicting chemical composition and fibre digestibility of hay-based total mixed rations. ITALIAN JOURNAL OF ANIMAL SCIENCE 2021. [DOI: 10.1080/1828051x.2021.1990804] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Giovanni Buonaiuto
- Dipartimento di Scienze Mediche Veterinarie, Alma Mater Studiorum - University of Bologna, Ozzano dell'Emilia (BO), Italy
| | - Damiano Cavallini
- Dipartimento di Scienze Mediche Veterinarie, Alma Mater Studiorum - University of Bologna, Ozzano dell'Emilia (BO), Italy
| | - Ludovica Maria Eugenia Mammi
- Dipartimento di Scienze Mediche Veterinarie, Alma Mater Studiorum - University of Bologna, Ozzano dell'Emilia (BO), Italy
| | - Francesca Ghiaccio
- Dipartimento di Scienze Mediche Veterinarie, Alma Mater Studiorum - University of Bologna, Ozzano dell'Emilia (BO), Italy
| | - Alberto Palmonari
- Dipartimento di Scienze Mediche Veterinarie, Alma Mater Studiorum - University of Bologna, Ozzano dell'Emilia (BO), Italy
| | - Andrea Formigoni
- Dipartimento di Scienze Mediche Veterinarie, Alma Mater Studiorum - University of Bologna, Ozzano dell'Emilia (BO), Italy
| | - Giulio Visentin
- Dipartimento di Scienze Mediche Veterinarie, Alma Mater Studiorum - University of Bologna, Ozzano dell'Emilia (BO), Italy
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Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review. SENSORS 2021; 21:s21093052. [PMID: 33925576 PMCID: PMC8123893 DOI: 10.3390/s21093052] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 11/16/2022]
Abstract
Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.
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Edwards K, Manley M, Hoffman LC, Williams PJ. Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud. Foods 2021; 10:foods10020448. [PMID: 33670564 PMCID: PMC7922372 DOI: 10.3390/foods10020448] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/05/2021] [Accepted: 02/09/2021] [Indexed: 12/04/2022] Open
Abstract
In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.
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Affiliation(s)
- Kiah Edwards
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (K.E.); (M.M.)
| | - Marena Manley
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (K.E.); (M.M.)
| | - Louwrens C. Hoffman
- Department of Animal Sciences, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; or
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Health and Food Sciences Precinct, 39 Kessels Rd, Coopers Plains 4108, Australia
| | - Paul J. Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (K.E.); (M.M.)
- Correspondence: ; Tel.: +27-21-808-3155
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Tyska D, Mallmann AO, Vidal JK, de Almeida CAA, Gressler LT, Mallmann CA. Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR). PLoS One 2021; 16:e0244957. [PMID: 33412558 PMCID: PMC7790530 DOI: 10.1371/journal.pone.0244957] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 12/18/2020] [Indexed: 11/19/2022] Open
Abstract
Fumonisins (FBs) and zearalenone (ZEN) are mycotoxins which occur naturally in grains and cereals, especially maize, causing negative effects on animals and humans. Along with the need for constant monitoring, there is a growing demand for rapid, non-destructive methods. Among these, Near Infrared Spectroscopy (NIR) has made great headway for being an easy-to-use technology. NIR was applied in the present research to quantify the contamination level of total FBs, i.e., fumonisin B1+fumonisin B2 (FB1+FB2), and ZEN in Brazilian maize. From a total of six hundred and seventy-six samples, 236 were analyzed for FBs and 440 for ZEN. Three regression models were defined: one with 18 principal components (PCs) for FB1, one with 10 PCs for FB2, and one with 7 PCs for ZEN. Partial least square regression algorithm with full cross-validation was applied as internal validation. External validation was performed with 200 unknown samples (100 for FBs and 100 for ZEN). Correlation coefficient (R), determination coefficient (R2), root mean square error of prediction (RMSEP), standard error of prediction (SEP) and residual prediction deviation (RPD) for FBs and ZEN were, respectively: 0.809 and 0.991; 0.899 and 0.984; 659 and 69.4; 682 and 69.8; and 3.33 and 2.71. No significant difference was observed between predicted values using NIR and reference values obtained by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS), thus indicating the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs and ZEN contamination. The external validation confirmed a fair potential of the model in predicting FB1+FB2 and ZEN concentration. This is the first study providing scientific knowledge on the determination of FBs and ZEN in Brazilian maize samples using NIR, which is confirmed as a reliable alternative methodology for the analysis of such toxins.
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Affiliation(s)
- Denize Tyska
- Department of Animal Health and Reproduction, Laboratory of Mycotoxicological Analyses (LAMIC), Federal University of Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil
| | | | - Juliano Kobs Vidal
- Department of Animal Health and Reproduction, Laboratory of Mycotoxicological Analyses (LAMIC), Federal University of Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil
| | - Carlos Alberto Araújo de Almeida
- Department of Animal Health and Reproduction, Laboratory of Mycotoxicological Analyses (LAMIC), Federal University of Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil
| | | | - Carlos Augusto Mallmann
- Department of Animal Health and Reproduction, Laboratory of Mycotoxicological Analyses (LAMIC), Federal University of Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil
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Hyslop J, Duthie CA, Rooke JA, Richardson RI. Meat and sensory eating quality of loin steaks from cattle slaughtered at different ages as a result of short, medium or long finishing systems. ANIMAL PRODUCTION SCIENCE 2021. [DOI: 10.1071/an20550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Context
In the belief that feed costs and use of cereal grains are reduced, production systems based on grazed and conserved forage in which cattle are slaughtered at 30 to 36 months of age are increasingly advocated.
Aims
As there is a scarcity of information concerning meat quality traits of beef from such extended rearing systems, three finishing systems of different lengths (Short, 14–16 months; Medium, 20–24 months; Long, 31–34 months) were compared in which cattle were slaughtered at similar conformation and fat classification scores.
Methods
The experiment comprised a three (system) × two (gender) design with 24 Limousin cross-bred cattle (12 steers and 12 heifers) assigned to each system. Characteristics of the rib section (between and inclusive of the 5th and 10th ribs) and sensory properties of M. longissimus thoracis samples were then assessed.
Key results
Older (Long system) cattle had greater rib section and L. thoracis weights. L. thoracis was tougher in older (Long system) cattle when assessed by a trained sensory panel. Heifers had lower rib section weights than steers but neither rib section composition or meat toughness differed between genders. Gristle (visible connective tissue) in the rib section increased with system length and was associated with an increased perception of gristle on eating.
Conclusions
Overall meat quality was considered commercially acceptable regardless of system.
Implications
The likely increased greenhouse gas emissions but reduced utilisation of food resources from the Long system need to be considered in conjunction with the meat quality characteristics of the system.
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Yakubu HG, Kovacs Z, Toth T, Bazar G. The recent advances of near-infrared spectroscopy in dairy production-a review. Crit Rev Food Sci Nutr 2020; 62:810-831. [PMID: 33043681 DOI: 10.1080/10408398.2020.1829540] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
One of the major issues confronting the dairy industry is the efficient evaluation of the quality of feed, milk and dairy products. Over the years, the use of rapid analytical methods in the dairy industry has become imperative. This is because of the documented evidence of adulteration, microbial contamination and the influence of feed on the quality of milk and dairy products. Because of the delays involved in the use of wet chemistry methods during the evaluation of these products, rapid analytical techniques such as near-infrared spectroscopy (NIRS) has gained prominence and proven to be an efficient tool, providing instant results. The technique is rapid, nondestructive, precise and cost-effective, compared with other laboratory techniques. Handheld NIRS devices are easily used on the farm to perform quality control measures on an incoming feed from suppliers, during feed preparation, milking and processing of cheese, butter and yoghurt. This ensures that quality feed, milk and other dairy products are obtained. This review considers research articles published in reputable journals which explored the possible application of NIRS in the dairy industry. Emphasis was on what quality parameters were easily measured with NIRS, and the limitations in some instances.
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Affiliation(s)
- Haruna Gado Yakubu
- Department of Nutritional Science and Production Technology, Faculty of Agricultural and Environmental Sciences, Szent István University, Kaposvár, Hungary
| | - Zoltan Kovacs
- Department of Physics and Control, Faculty of Food Science, Szent István University, Budapest, Hungary
| | - Tamas Toth
- Agricultural and Food Research Centre, Széchenyi István University, Győr, Hungary.,Adexgo Kft, Balatonfüred, Hungary
| | - George Bazar
- Department of Nutritional Science and Production Technology, Faculty of Agricultural and Environmental Sciences, Szent István University, Kaposvár, Hungary.,Adexgo Kft, Balatonfüred, Hungary
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15
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Petit Bon M, Böhner H, Kaino S, Moe T, Bråthen KA. One leaf for all: Chemical traits of single leaves measured at the leaf surface using near‐infrared reflectance spectroscopy. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13432] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Matteo Petit Bon
- Department of Arctic and Marine Biology UiT ‐ The Arctic University of Norway Tromsø Norway
- Department of Arctic Biology University Centre in Svalbard (UNIS) Longyearbyen Norway
| | - Hanna Böhner
- Department of Arctic and Marine Biology UiT ‐ The Arctic University of Norway Tromsø Norway
| | - Sissel Kaino
- Department of Arctic and Marine Biology UiT ‐ The Arctic University of Norway Tromsø Norway
| | - Torunn Moe
- Department of Arctic and Marine Biology UiT ‐ The Arctic University of Norway Tromsø Norway
| | - Kari Anne Bråthen
- Department of Arctic and Marine Biology UiT ‐ The Arctic University of Norway Tromsø Norway
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16
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Fowler SM, Morris S, Hopkins DL. Preliminary investigation for the prediction of intramuscular fat content of lamb in-situ using a hand- held NIR spectroscopic device. Meat Sci 2020; 166:108153. [PMID: 32330832 DOI: 10.1016/j.meatsci.2020.108153] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/15/2020] [Accepted: 04/15/2020] [Indexed: 01/02/2023]
Abstract
Intramuscular fat (IMF) content is critical in the determination of eating quality. At present the Australian lamb industry has no ability to measure IMF as carcases are not split and processing speeds of up to 15 animals per minute prohibit the use of traditional methods. Consequently, the potential for a hand-held Near- Infrared (NIR) device to predict the IMF content of lamb topside in-situ was investigated. Models demonstrated that there is an ability to predict the IMF content of topside (R2 = 0.58, RMSEP = 0.85) using NIR spectra collected at 24 h post-mortem and loin (R2 = 0.50, RMSEP = 0.91). However, the models were limited by the range and distribution of the lamb population measured. Thus, further research is required to determine whether these models can be improved by increasing the range of data in the calibration models and considering alternate methods of analysis which are suitable for skewed populations.
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Affiliation(s)
- Stephanie M Fowler
- Cooperative Research Centre for Sheep Innovation, Armidale NSW 2350, Australia; NSW Department of Primary Industries, Centre for Red Meat and Sheep Development, Cowra NSW 2794, Australia.
| | - Stephen Morris
- Wollongbar Primary Industries Institute, NSW Department of Primary Industries, Wollongbar NSW 2477, Australia
| | - David L Hopkins
- Cooperative Research Centre for Sheep Innovation, Armidale NSW 2350, Australia; NSW Department of Primary Industries, Centre for Red Meat and Sheep Development, Cowra NSW 2794, Australia
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17
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Femenias A, Gatius F, Ramos AJ, Sanchis V, Marín S. Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review. Food Control 2020. [DOI: 10.1016/j.foodcont.2019.106819] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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18
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Hong AL, Ispiryan M, Padalkar MV, Jones BC, Batzdorf AS, Shetye SS, Pleshko N, Rajapakse CS. MRI-derived bone porosity index correlates to bone composition and mechanical stiffness. Bone Rep 2019; 11:100213. [PMID: 31372372 PMCID: PMC6660551 DOI: 10.1016/j.bonr.2019.100213] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 06/13/2019] [Accepted: 06/19/2019] [Indexed: 01/11/2023] Open
Abstract
The MRI-derived porosity index (PI) is a non-invasively obtained biomarker based on an ultrashort echo time sequence that images both bound and pore water protons in bone, corresponding to water bound to organic collagenous matrix and freely moving water, respectively. This measure is known to strongly correlate with the actual volumetric cortical bone porosity. However, it is unknown whether PI may also be able to directly quantify bone organic composition and/or mechanical properties. We investigated this in human cadaveric tibiae by comparing PI values to near infrared spectral imaging (NIRSI) compositional data and mechanical compression data. Data were obtained from a cohort of eighteen tibiae from male and female donors with a mean ± SD age of 70 ± 21 years. Biomechanical stiffness in compression and NIRSI-derived collagen and bound water content all had significant inverse correlations with PI (r = −0.79, −0.73, and −0.95 and p = 0.002, 0.007, and <0.001, respectively). The MRI-derived bone PI alone was a moderate predictor of bone stiffness (R2 = 0.63, p = 0.002), and multivariate analyses showed that neither cortical bone cross-sectional area nor NIRSI values improved bone stiffness prediction compared to PI alone. However, NIRSI-obtained collagen and water data together were a moderate predictor of bone stiffness (R2 = 0.52, p = 0.04). Our data validates the MRI-derived porosity index as a strong predictor of organic composition of bone and a moderate predictor of bone stiffness, and also provides preliminary evidence that NIRSI measures may be useful in future pre-clinical studies on bone pathology.
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Affiliation(s)
- Abigail L Hong
- Department of Radiology, University of Pennsylvania, United States of America
| | - Mikayel Ispiryan
- Department of Radiology, University of Pennsylvania, United States of America
| | - Mugdha V Padalkar
- Department of Bioengineering, Temple University, United States of America
| | - Brandon C Jones
- Department of Radiology, University of Pennsylvania, United States of America.,Department of Orthopaedic Surgery, University of Pennsylvania, United States of America
| | | | - Snehal S Shetye
- Department of Orthopaedic Surgery, University of Pennsylvania, United States of America
| | - Nancy Pleshko
- Department of Bioengineering, Temple University, United States of America
| | - Chamith S Rajapakse
- Department of Radiology, University of Pennsylvania, United States of America.,Department of Orthopaedic Surgery, University of Pennsylvania, United States of America
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19
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Murguzur FJA, Bison M, Smis A, Böhner H, Struyf E, Meire P, Bråthen KA. Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content. Sci Rep 2019; 9:8259. [PMID: 31164672 PMCID: PMC6547662 DOI: 10.1038/s41598-019-44558-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 05/14/2019] [Indexed: 11/09/2022] Open
Abstract
Near-infrared spectroscopy (NIRS) is a high-throughput technology with potential to infer nitrogen (N), phosphorus (P) and carbon (C) content of all vascular plants based on empirical calibrations with chemical analysis, but is currently limited to the sample populations upon which it is based. Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content. We found calibration models to perform well (R2validation = 0.94 and RMSEP = 0.20% for N, R2validation = 0.76 and RMSEP = 0.05% for P and R2validation = 0.82 and RMSEP = 1.16% for C), integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). Furthermore, when applied for predicting foliar N, P and C content in samples from a new biogeographic region (Svalbard), our arctic-alpine NIRS model performed well. The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.
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Affiliation(s)
| | - Marjorie Bison
- Laboratoire d'Ecologie Alpine, Université de Savoie, 73376, Le Bourget du Lac, France
| | - Adriaan Smis
- Department of Arctic and Marine Biology, UiT Arctic University of Norway, N-9037, Tromsø, Norway
- Ecosystem Management Research Group, University of Antwerp, B-2610, Antwerp, Belgium
| | - Hanna Böhner
- Department of Arctic and Marine Biology, UiT Arctic University of Norway, N-9037, Tromsø, Norway
| | - Eric Struyf
- Ecosystem Management Research Group, University of Antwerp, B-2610, Antwerp, Belgium
| | - Patrick Meire
- Ecosystem Management Research Group, University of Antwerp, B-2610, Antwerp, Belgium
| | - Kari Anne Bråthen
- Department of Arctic and Marine Biology, UiT Arctic University of Norway, N-9037, Tromsø, Norway.
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20
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Peiris KHS, Bean SR, Chiluwal A, Perumal R, Jagadish SVK. Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration. Cereal Chem 2019. [DOI: 10.1002/cche.10164] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Scott R. Bean
- USDA‐ARS Center for Grain and Animal Health Research Manhattan Kansas
| | - Anuj Chiluwal
- Department of Agronomy Kansas State University Manhattan Kansas
| | - Ramasamy Perumal
- Kansas State University Agricultural Research Center Hays Kansas
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21
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Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents. REMOTE SENSING 2019. [DOI: 10.3390/rs11070799] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.
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22
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Fabà L, Solà-Oriol D, Balfagon A, Coma J, Gasa J. Assessing the effect of ingredients variability on the composition of the final complete feed for swine. CANADIAN JOURNAL OF ANIMAL SCIENCE 2019. [DOI: 10.1139/cjas-2017-0157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
To characterize the variability of 11 feed ingredients and their impact on the final feed, 728 ingredient samples were collected during 5 months in a feed-plant and were analyzed by near-infrared spectrophotometry (NIRS). Six diets for fattening pigs and gestating sows were formulated using regional information of ingredient chemical composition (reference): LIM, limited; EU, common European; and MULT, multi-ingredient; respectively, including 5, 7, and 10 ingredients. The formulas were replicated 15 times using actual chemical composition (NIRS) from three samples per ingredient and month. This theoretical procedure was validated through small-scale manufacturing 30 LIM-diets, which samples were proximal (PA) and NIRS analyzed for dry matter and crude protein (CP) contents. Those mixtures were also PA analyzed. The ingredients showed coefficient of variation (CV %) higher for crude fiber (CF) (2.6%–18.3%) than CP (2.0%–9.3%). Comparing all diets for all chemical components, variability was reduced when including more ingredients from 0.5%–5.5% to 0.3%–2.6% CV. In most cases, the actual chemical composition of the diets underestimated their reference formula (1.3%–10.8%, CP and CF). A deviation from the targeted diet occurs if variability is not regarded. Therefore, a proper method to predict ingredient composition and nutritional value before use may increase the accuracy of diet formulation between 2% and 10%.
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Affiliation(s)
- Lluís Fabà
- Animal Nutrition and Welfare Service, Department of Animal and Food Sciences, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain
| | - David Solà-Oriol
- Animal Nutrition and Welfare Service, Department of Animal and Food Sciences, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain
| | - Aitor Balfagon
- Cargill Animal Nutrition, Mequinenza, Zaragoza 50170, Spain
| | - Jaume Coma
- Grupo Vall Companys, Polígono El Segre 410, Lleida 25191, Spain
| | - Josep Gasa
- Animal Nutrition and Welfare Service, Department of Animal and Food Sciences, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain
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23
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Jimenez R, Molina L, Zarei I, Lapis JR, Chavez R, Cuevas RPO, Sreenivasulu N. Method Development of Near-Infrared Spectroscopy Approaches for Nondestructive and Rapid Estimation of Total Protein in Brown Rice Flour. Methods Mol Biol 2019; 1892:109-135. [PMID: 30397803 DOI: 10.1007/978-1-4939-8914-0_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Rice varietal development and improvement programs are constantly seeking means to shorten the breeding cycle in order to deliver new, consumer-acceptable rice varieties to farmers and to consumers. Advances in molecular biology technologies have enabled breeders to use high-throughput genotyping to screen breeding lines. However, current phenotyping technologies, particularly for rice cooking and eating properties, have yet to match the efficiency of genotyping methodologies. A high-throughput and cost-effective phenotyping suite is essential because without phenotype, the value of genotypic information cannot be maximized. In this book chapter, we explore the application of near-infrared spectroscopy (NIRS), a high-throughput and nondestructive approach in characterizing rice grains, primarily describing method development and validation, instrument calibration, upgrading, and maintenance. We then focus on estimating protein content (PC) in brown rice as a case study because (1) PC is an attribute that contributes to the cooking behavior and the eating properties of cooked rice; and (2) proteins contain chemical bonds that can easily be detected by NIRS.
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Affiliation(s)
- Rosario Jimenez
- International Rice Research Institute, Los Baños, Laguna, Philippines
| | - Lilia Molina
- International Rice Research Institute, Los Baños, Laguna, Philippines
| | - Iman Zarei
- International Rice Research Institute, Los Baños, Laguna, Philippines
| | | | - Ruben Chavez
- International Rice Research Institute, Los Baños, Laguna, Philippines
| | | | - Nese Sreenivasulu
- International Rice Research Institute, Los Baños, Laguna, Philippines.
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24
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Zhu Z, Chen S, Wu X, Xing C, Yuan J. Determination of soybean routine quality parameters using near-infrared spectroscopy. Food Sci Nutr 2018; 6:1109-1118. [PMID: 29983975 PMCID: PMC6021721 DOI: 10.1002/fsn3.652] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 03/17/2018] [Accepted: 03/25/2018] [Indexed: 11/12/2022] Open
Abstract
Large differences in quality existed between soybean samples. In order to rapidly detect soybean quality between samples from different areas, we have developed near-infrared spectroscopy (NIRS) models for the moisture, crude fat, and protein content of soybeans, based on 360 soybean samples collected from different areas. Compared with whole kernels, soybean powder with particle sizes of 60 mesh was more suitable for modeling of moisture, crude fat, and protein content. To increase the reproducibility of the prediction model, uniform particle sizes of soybeans were prepared by grinding and sieving soybeans with different sizes and colors. Modeling analysis showed that the internal cross-validation correlation coefficients (Rcv) for the moisture, crude fat, and protein content of soybeans were .965, .941, and .949, respectively, and the determination coefficients (R2) were .966, .958, and .958. NIRS performed well as a rapid method for the determination of routine quality parameters and provided reference data for the analysis of soybean quality using FT-NIRS.
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Affiliation(s)
- Zhenying Zhu
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
| | - Shangbing Chen
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
| | - Xueyou Wu
- School of Food Science and TechnologyJiangnan UniversityWuxiChina
| | - Changrui Xing
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
| | - Jian Yuan
- College of Food Science and Engineering, Collaborative Innovation Center for Modern Grain Circulation and SafetyKey Laboratory of Grains and Oils Quality Control and ProcessingNanjing University of Finance and EconomicsNanjingChina
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25
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Levasseur-Garcia C. Updated Overview of Infrared Spectroscopy Methods for Detecting Mycotoxins on Cereals (Corn, Wheat, and Barley). Toxins (Basel) 2018; 10:E38. [PMID: 29320435 PMCID: PMC5793125 DOI: 10.3390/toxins10010038] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 12/21/2017] [Accepted: 01/03/2018] [Indexed: 12/03/2022] Open
Abstract
Each year, mycotoxins cause economic losses of several billion US dollars worldwide. Consequently, methods must be developed, for producers and cereal manufacturers, to detect these toxins and to comply with regulations. Chromatographic reference methods are time consuming and costly. Thus, alternative methods such as infrared spectroscopy are being increasingly developed to provide simple, rapid, and nondestructive methods to detect mycotoxins. This article reviews research conducted over the last eight years into the use of near-infrared and mid-infrared spectroscopy to monitor mycotoxins in corn, wheat, and barley. More specifically, we focus on the Fusarium species and on the main fusariotoxins of deoxynivalenol, zearalenone, and fumonisin B1 and B2. Quantification models are insufficiently precise to satisfy the legal requirements. Sorting models with cutoff levels are the most promising applications.
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26
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Rajapakse CS, Padalkar MV, Yang HJ, Ispiryan M, Pleshko N. Non-destructive NIR spectral imaging assessment of bone water: Comparison to MRI measurements. Bone 2017; 103:116-124. [PMID: 28666972 PMCID: PMC5572678 DOI: 10.1016/j.bone.2017.06.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 05/07/2017] [Accepted: 06/21/2017] [Indexed: 01/22/2023]
Abstract
Bone fracture risk increases with age, disease states, and with use of certain therapeutics, such as acid-suppressive drugs, steroids and high-dose bisphosphonates. Historically, investigations into factors that underlie bone fracture risk have focused on evaluation of bone mineral density (BMD). However, numerous studies have pointed to factors other than BMD that contribute to fragility, including changes in bone collagen and water. The goal of this study is to investigate the feasibility of using near infrared spectral imaging (NIRSI) to determine the spatial distribution and relative amount of water and organic components in whole cross-sections of bone, and to compare those results to those obtained using magnetic resonance imaging (MRI) methods. Cadaver human whole-section tibiae samples harvested from 18 donors of ages 27-97years underwent NIRSI and ultrashort echo time (UTE) MRI. As NIRSI data is comprised of broad absorbances, second derivative processing was evaluated as a means to narrow peaks and obtain compositional information. The (inverted) second derivative peak heights of the NIRSI absorbances correlated significantly with the mean peak integration of the water, collagen and fat NIR absorbances, respectively, indicating that either processing method could be used for compositional assessment. The 5797cm-1 absorbance was validated as arising from the fat present in bone marrow, as it completely disappeared after ultrasonication. The MRI UTE-determined bound water content in tibial cortical bone samples ranged from 62 to 91%. The NIRSI water peaks at 5152cm-1 and at 7008cm-1 correlated significantly with the UTE data, with r=0.735, p=0.016, and r=0.71, p=0.0096, respectively. There was also a strong correlation between the intensity of the NIRSI water peak at 7008cm-1 and the intensity of the collagen peak at 4608cm-1 (r=0.69, p=0.004). Since NIRSI requires minimal to no sample preparation, this approach has great potential to become a gold standard modality for the investigation of changes in water content, distribution, and environment in pre-clinical studies of bone pathology and therapeutics.
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Affiliation(s)
- Chamith S Rajapakse
- Departments of Radiology and Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Mugdha V Padalkar
- Department of Bioengineering, Temple University, 1947 N. 12th St, Philadelphia, PA, USA
| | - Hee Jin Yang
- Department of Bioengineering, Temple University, 1947 N. 12th St, Philadelphia, PA, USA
| | - Mikayel Ispiryan
- Departments of Radiology and Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Nancy Pleshko
- Department of Bioengineering, Temple University, 1947 N. 12th St, Philadelphia, PA, USA.
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27
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Orina I, Manley M, Williams PJ. Non-destructive techniques for the detection of fungal infection in cereal grains. Food Res Int 2017; 100:74-86. [PMID: 28873744 DOI: 10.1016/j.foodres.2017.07.069] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 07/31/2017] [Accepted: 07/31/2017] [Indexed: 10/19/2022]
Abstract
Infection of cereal grains by fungi is a serious problem worldwide. Depending on the environmental conditions, cereal grains may be colonised by different species of fungi. These fungi cause reduction in yield, quality and nutritional value of the grain; and of major concern is their production of mycotoxins which are harmful to both humans and animals. Early detection of fungal contamination is an essential control measure for ensuring storage longevity and food safety. Conventional methods for detection of fungal infection, such as culture and colony techniques or immunological methods are either slow, labour intensive or difficult to automate. In recent years, there has been an increasing need to develop simple, rapid, non-destructive methods for early detection of fungal infection and mycotoxins contamination in cereal grains. Methods such as near infrared (NIR) spectroscopy, NIR hyperspectral imaging, and electronic nose were evaluated for these purposes. This paper reviews the different non-destructive techniques that have been considered thus far for detection of fungal infection and mycotoxins in cereal grains, including their principles, application and limitations.
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Affiliation(s)
- Irene Orina
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; Department of Food Science and Technology, Jomo Kenyatta University of Agriculture and Technology, P. O. Box 62000, Nairobi, Kenya
| | - Marena Manley
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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28
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Affiliation(s)
- Sam W. Coleman
- CSIRO Tropical Agriculture; Indooroopilly Queensland Australia
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29
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Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products. FOOD BIOPROCESS TECH 2016. [DOI: 10.1007/s11947-016-1817-8] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Mureșan V, Danthine S, Mureșan AE, Racolța E, Blecker C, Muste S, Socaciu C, Baeten V. In situ analysis of lipid oxidation in oilseed-based food products using near-infrared spectroscopy and chemometrics: The sunflower kernel paste (tahini) example. Talanta 2016; 155:336-46. [DOI: 10.1016/j.talanta.2016.04.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 04/06/2016] [Accepted: 04/08/2016] [Indexed: 10/22/2022]
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de Godoy MR, Hervera M, Swanson KS, Fahey GC. Innovations in Canine and Feline Nutrition: Technologies for Food and Nutrition Assessment. Annu Rev Anim Biosci 2016; 4:311-33. [DOI: 10.1146/annurev-animal-021815-111414] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Pet owners have increasing concerns about the nutrition of their pets, and they desire foods and treats that are safe, traceable, and of high nutritive value. To meet these high expectations, detailed chemical composition characterization of ingredients well beyond that provided by proximate analysis will be required, as will information about host physiology and metabolism. Use of faster and more precise analytical methodology and novel technologies that have the potential to improve pet food safety and quality will be implemented. In vitro and in vivo assays will continue to be used as screening tools to evaluate nutrient quality and adequacy in novel ingredients prior to their use in animal diets. The use of molecular and high-throughput technologies allows implementation of noninvasive studies in dogs and cats to investigate the impact of dietary interventions by using systems biology approaches. These approaches may further improve the health and longevity of pets.
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Affiliation(s)
- Maria R.C. de Godoy
- Department of Animal Sciences, University of Illinois, Urbana, Illinois 61801;, ,
| | | | - Kelly S. Swanson
- Department of Animal Sciences, University of Illinois, Urbana, Illinois 61801;, ,
| | - George C. Fahey
- Department of Animal Sciences, University of Illinois, Urbana, Illinois 61801;, ,
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An overview on principle, techniques and application of hyperspectral imaging with special reference to ham quality evaluation and control. Food Control 2014. [DOI: 10.1016/j.foodcont.2014.05.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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An optimization strategy for waveband selection in FT-NIR quantitative analysis of corn protein. J Cereal Sci 2014. [DOI: 10.1016/j.jcs.2014.07.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Chen H, Ai W, Feng Q, Jia Z, Song Q. FT-NIR spectroscopy and Whittaker smoother applied to joint analysis of duel-components for corn. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2014; 118:752-759. [PMID: 24140791 DOI: 10.1016/j.saa.2013.09.065] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2013] [Revised: 09/09/2013] [Accepted: 09/25/2013] [Indexed: 06/02/2023]
Abstract
Protein and total fat are two ingredients to measure the quality of corn. The aim of this study is to evaluate the quality of corn by the dual-component join determination through Fourier transform near infrared (FT-NIR) spectroscopic analysis. The calibration models were established by the systematic study performed respectively in the four regions of the whole range, the second overtone, the first overtone, and the combination. Whittaker smoother was introduced as an attractive alternative data preprocessing method. With the optimized parameters, Whittaker smoother indicates its priority for improving modeling results in any of the four regions. The predictive abilities were compared between the joint analysis of protein and total fat and the separate analysis of each single component by partial least squares (PLS) modeling. The uncertainty in parameter was further estimated for the linear models. It is suggested that the joint analysis of dual-component always leads to better predictive results, and also provided good evaluation results for the independent validation samples. For the joint analysis, the optimal region for protein was the combination (5400-4000 cm(-1)), and the optimal region for total fat was the first overtone (7200-5400 cm(-1)). The optimal PLS models also provided appreciate predictive performance for both protein and total fat. And the parameter uncertainty determination provided an acceptable estimate of the measured uncertainty for the FT-NIR analysis of corn. In general, the joint analysis of dual-component is a better strategy for FT-NIR analysis of corn, and it is hoped to be tested for other objects.
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Affiliation(s)
- Huazhou Chen
- College of Science, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Spatial Information and Geomatics (Guilin University of Technology), Guilin 541004, China.
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Abstract
Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible-near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R 2CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R 2CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R 2CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R 2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.
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Gaspardo B, Del Zotto S, Torelli E, Cividino S, Firrao G, Della Riccia G, Stefanon B. A rapid method for detection of fumonisins B1 and B2 in corn meal using Fourier transform near infrared (FT-NIR) spectroscopy implemented with integrating sphere. Food Chem 2012; 135:1608-12. [DOI: 10.1016/j.foodchem.2012.06.078] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2010] [Revised: 04/16/2012] [Accepted: 06/24/2012] [Indexed: 11/16/2022]
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Elmasry G, Kamruzzaman M, Sun DW, Allen P. Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review. Crit Rev Food Sci Nutr 2012; 52:999-1023. [DOI: 10.1080/10408398.2010.543495] [Citation(s) in RCA: 225] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Yegani M, Korver DR. Review: Prediction of variation in energetic value of wheat for poultry. CANADIAN JOURNAL OF ANIMAL SCIENCE 2012. [DOI: 10.4141/cjas2011-114] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Yegani, M. and Korver, D. R. 2012. Review: Prediction of variation in energetic value of wheat for poultry. Can. J. Anim. Sci. 92: 261–273. Variations in physical and chemical characteristics of wheat can significantly influence the energy availability of this feed ingredient for poultry. These variations can result in inefficiencies in the form of over- or under-formulation of the diets at commercial feed mills or on poultry farms. Therefore, having a clear understanding of the variations is of paramount importance in the formulation of poultry diets as they can have negative consequences for production performance of birds. There are a large number of factors that can contribute to variations in energy availability of wheat for poultry. This review is intended to briefly discuss these factors and also practical approaches that can be used to predict these variations. These approaches include measuring physico-chemical characteristics, in vivo digestibility trials, in vitro digestibility techniques, and near infrared reflectance spectroscopy (NIRS). There are limitations associated with physico-chemical and in vivo measurements. However, in vitro digestibility techniques are simple and fast and can provide data for database development and ongoing calibrations of NIRS systems. Near infrared reflectance spectroscopy has enormous potential to predict variations in wheat apparent metabolizable energy, leading to more accurate diet formulation.
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Affiliation(s)
- M. Yegani
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5
| | - D. R. Korver
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2P5
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Rivero M, Valderrama X, Haines D, Alomar D. Prediction of immunoglobulin G content in bovine colostrum by near-infrared spectroscopy. J Dairy Sci 2012; 95:1410-8. [DOI: 10.3168/jds.2011-4532] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2011] [Accepted: 11/03/2011] [Indexed: 11/19/2022]
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Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS). Food Chem 2011; 126:1817-20. [DOI: 10.1016/j.foodchem.2010.12.078] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2010] [Revised: 11/14/2010] [Accepted: 12/16/2010] [Indexed: 11/20/2022]
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Nie Z, Han J, Liu T, Liu X. Hot topic: application of support vector machine method in prediction of alfalfa protein fractions by near infrared reflectance spectroscopy. J Dairy Sci 2008; 91:2361-9. [PMID: 18487658 DOI: 10.3168/jds.2008-0985] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The object of this study was to explore the potential for support vector machine (SVM) to improve the precision of predicting protein fractions by near infrared reflectance spectroscopy (NIRS). Generally, most protein fractions determined in Cornell Net Carbohydrate and Protein System (CNCPS), especially the neutral detergent insoluble protein (NDFCP) and acid detergent insoluble protein (ADFCP), could not be accurately predicted by the commonly used partial least squares (PLS) method. A recently developed chemometric method, SVM, was applied in NIRS prediction of alfalfa protein fractions in this study. Two hundred thirty alfalfa samples were scanned on a near infrared reflectance spectrophotometer, and analyzed for crude protein (CP), true protein precipitated in tungstic acid (TCP), borate-phosphate buffer-insoluble protein (BICP), NDFCP, and ADFCP. These 5 laboratory proteins and the CNCPS protein fractions A, B1, B2, B3, and C were predicted by NIRS using the PLS and SVM methods. According to PLS-NIRS regression, CP, TCP, BICP, A, and B2 obtained the determination coefficient of prediction (R(p)(2)) of 0.96, 0.91, 0.94, 0.94, and 0.93, and the ratios of standard deviation of prediction samples: standard error of prediction samples (RPD) values were 5.07, 3.31, 3.98, 3.96, and 3.91. Neutral detergent insoluble protein, ADFCP (fraction C), B1, and B3 were predicted with R(p)(2) of 0.75, 0.83, 0.30, and 0.62, and RPD values of 1.98, 2.42, 1.20, and 1.62; Calibrated by the SVM-NIRS method, R(p)(2) values of CP, TCP, BICP, NDFCP, ADFCP(C), A, and B2 achieved 0.99, 0.97, 0.97, 0.90, 0.93, 0.97, and 0.97, respectively. The RPD values of those fractions were 8.68, 8.26, 6.11, 3.08, 3.69, 5.97, and 5.81, respectively. The R(p)(2) and RPD values of fractions B1 and B3 were 2.67 and 0.87 (B1) and 2.51 and 0.75 (B3) directly predicted by SVM-NIRS model. In this study, the chemical analysis results of B1 and B3 were also correlated with calculated results from TCP-BICP and NDFCP-ADFCP, which were predicted by SVM-NIRS models. The B1 protein fraction achieved R(p)(2) and RPD values of 0.87 and 3.61, whereas values for B2 were 0.75 and 2.00. Data suggested that use of SVM methods in NIRS technology could improve the accuracy of predicting protein fractions. This study showed the potential of increasing the NIRS prediction accuracy to a level of practical use for all protein fractions, except B3.
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Affiliation(s)
- Z Nie
- Department of Grassland Science, College of Animal Science and Technology, China Agricultural University, Beijing 10094, China
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Kulmyrzaev A, Karoui R, De Baerdemaeker J, Dufour E. Infrared and Fluorescence Spectroscopic Techniques for the Determination of Nutritional Constituents in Foods. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2007. [DOI: 10.1080/10942910601045305] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Foster JG, Clapham WM, Fedders JM. Quantification of fatty acids in forages by near-infrared reflectance spectroscopy. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2006; 54:3186-92. [PMID: 16637670 DOI: 10.1021/jf052398u] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Near-infrared reflectance spectroscopy (NIRS) was evaluated as a possible alternative to gas chromatography (GC) for the quantitative analysis of fatty acids in forages. Herbage samples from 11 greenhouse-grown forage species (grasses, legumes, and forbs) were collected at three stages of growth. Samples were freeze-dried, ground, and analyzed by GC and NIRS techniques. Half of the 195 samples were used to develop an NIRS calibration file for each of eight fatty acids, with the remaining half used as a validation data set. Spectral data, collected over a wavelength range of 1100-2498 nm, were regressed against GC data to develop calibration equations for lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2), and alpha-linolenic (C18:3) acids. Calibration equations had high coefficients of determination for calibration (0.93-0.99) and cross-validation (0.89-0.98), and standard errors of calibration and cross-validation were < 20% of the respective means. Simple linear regressions of NIRS results against GC data for the validation data set had r2 values ranging from 0.86 to 0.97. Regression slopes for C12:0, C14:0, C16:0, C18:0, C16:1, C18:2, and C18:3 were not significantly different (P = 0.05) from 1.0. The regression slope for C18:1 was 1.1. The ratio of standard error of prediction to standard deviation was > 3.0 for all fatty acids except C12:0 (2.6) and C14:0 (2.9). Validation statistics indicate that NIRS has high prediction ability for fatty acids in forages. Calibration equations developed using data for all plant materials accurately predicted concentrations of C16:0, C18:2, and C18:3 in individual plant species. Accuracy of prediction was less, but acceptable, for fatty acids (C12:0, C14:0, C18:0, C16:1, and C18:1) that were less prevalent.
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Affiliation(s)
- Joyce G Foster
- Appalachian Farming Systems Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beaver, West Virginia 25813-9423, USA.
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Karoui R, Kemps B, Bamelis F, De Ketelaere B, Decuypere E, De Baerdemaeker J. Methods to evaluate egg freshness in research and industry: A review. Eur Food Res Technol 2005. [DOI: 10.1007/s00217-005-0145-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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46
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Lovett D, Deaville E, Givens D, Finlay M, Owen E. Near infrared reflectance spectroscopy (NIRS) to predict biological parameters of maize silage: effects of particle comminution, oven drying temperature and the presence of residual moisture. Anim Feed Sci Technol 2005. [DOI: 10.1016/j.anifeedsci.2005.02.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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47
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Boval M, Coates D, Lecomte P, Decruyenaere V, Archimède H. Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle. Anim Feed Sci Technol 2004. [DOI: 10.1016/j.anifeedsci.2003.12.009] [Citation(s) in RCA: 49] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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48
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49
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Karoui R, Mazerolles G, Dufour É. Spectroscopic techniques coupled with chemometric tools for structure and texture determinations in dairy products. Int Dairy J 2003. [DOI: 10.1016/s0958-6946(03)00076-1] [Citation(s) in RCA: 80] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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