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Dewantier GR, Torley PJ, Blanch EW. Identifying Chemical Differences in Cheddar Cheese Based on Maturity Level and Manufacturer Using Vibrational Spectroscopy and Chemometrics. Molecules 2023; 28:8051. [PMID: 38138541 PMCID: PMC10745544 DOI: 10.3390/molecules28248051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/03/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
Cheese is a nutritious dairy product and a valuable commodity. Internationally, cheddar cheese is produced and consumed in large quantities, and it is the main cheese variety that is exported from Australia. Despite its importance, the analytical methods to that are used to determine cheese quality rely on traditional approaches that require time, are invasive, and which involve potentially hazardous chemicals. In contrast, spectroscopic techniques can rapidly provide molecular information and are non-destructive, fast, and chemical-free methods. Combined with partner recognition methods (chemometrics), they can identify small changes in the composition or condition of cheeses. In this work, we combined FTIR and Raman spectroscopies with principal component analysis (PCA) to investigate the effects of aging in commercial cheddar cheeses. Changes in the amide I and II bands were the main spectral characteristics responsible for classifying commercial cheddar cheeses based on the ripening time and manufacturer using FTIR, and bands from lipids, including β'-polymorph of fat crystals, were more clearly determined through changes in the Raman spectra.
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Affiliation(s)
- Gerson R. Dewantier
- Applied Chemistry and Environmental Science, School of Science, Royal Melbourne Institute of Technology University, Melbourne, VIC 3001, Australia;
| | - Peter J. Torley
- Biosciences and Food Technology, School of Science, Royal Melbourne Institute of Technology University, Bundoora, VIC 3083, Australia;
| | - Ewan W. Blanch
- Applied Chemistry and Environmental Science, School of Science, Royal Melbourne Institute of Technology University, Melbourne, VIC 3001, Australia;
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Di Donato F, Biancolillo A, Foschi M, D’archivio AA. Application of SPORT algorithm on ATR-FTIR data: A rapid and green tool for the characterization and discrimination of three typical Italian Pecorino cheeses. J Food Compost Anal 2022; 114:104784. [DOI: 10.1016/j.jfca.2022.104784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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3
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Ozturk M, Dogan MA, Menevseoglu A, Ayvaz H. Infrared spectroscopy combined with chemometrics as a convenient method to detect adulterations in cooking/stretching process in commercial cheese. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2021.105312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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4
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Affiliation(s)
- Larissa K R Silva
- Center for Biological and Health Sciences Federal University of Western Bahia Campus Universitário Barreiras Bahia CEP 47810‐047Brazil
| | - Leandro S Santos
- Program in Food Engineering and Science State University of Bahia Southwest Campus Universitário Itapetinga Bahia CEP 45700‐000 Brazil
| | - Sibelli P B Ferrão
- Program in Food Engineering and Science State University of Bahia Southwest Campus Universitário Itapetinga Bahia CEP 45700‐000 Brazil
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5
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Yaman H, Aykas DP, Jiménez-Flores R, Rodriguez-Saona LE. Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers. J Dairy Sci 2021; 105:40-55. [PMID: 34696910 DOI: 10.3168/jds.2021-20313] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 09/03/2021] [Indexed: 11/19/2022]
Abstract
Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (RPre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.
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Affiliation(s)
- Hulya Yaman
- Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus 43210; Department of Food Processing, Bolu Abant Izzet Baysal University, Bolu, Turkey 14100
| | - Didem P Aykas
- Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus 43210; Department of Food Engineering, Faculty of Engineering, Adnan Menderes University, Aydin, 09100, Turkey
| | - Rafael Jiménez-Flores
- Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus 43210
| | - Luis E Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus 43210.
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6
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Ayvaz H, Mortas M, Dogan MA, Atan M, Yildiz Tiryaki G, Karagul Yuceer Y. Near- and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species. J Food Sci Technol 2021; 58:3981-92. [PMID: 34471322 DOI: 10.1007/s13197-020-04861-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 09/23/2020] [Accepted: 10/14/2020] [Indexed: 10/23/2022]
Abstract
This study aimed to evaluate the performance of both near-infrared (NIR) diffuse reflectance and mid-infrared-attenuated total reflectance (MIR-ATR) in determining some quality parameters of a commercial white cheese made of unknown ratios of various milk species. For this purpose, 81 commercial Ezine cheese samples, a special ripened cheese produced in Turkey, containing unknown ratios of bovine, caprine, and ovine milk, were used. Reference analyses, including textural properties, protein content, nitrogen fractions, ripening index coefficients, fat, salt, dry matter-moisture, and ash contents as well as pH and titratable acidity levels, were conducted in the samples following the traditional gold standards. For NIR applications, the spectra of both intact cubes and hand-crushed cheese samples were collected, whereas the spectra of only hand-crushed cheese samples were collected for MIR-ATR. PLSR (Partial Least Squares Regression) calibration models were developed for each parameter (n = 61) and then validated using both cross-validation (leave-one-out approach) and an external validation set (n = 20). Overall, PLSR models developed for total protein, fat, salt, dry matter, moisture, and ash content, as well as pH and titratable acidity, yielded satisfactory performance statistics in the complementary use of NIR and MIR spectroscopy. However, PLSR models of the other parameters, including textural properties, nitrogen fractions, and the ripening index, could only separate high and low values and were not able to make accurate quantitative predictions. NIR spectroscopy was found to be more accurate than that of MIR-ATR spectroscopy for almost all the parameters except for pH and titratable acidity, for which MIR-ATR spectroscopy was superior.
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Tallarico S, Bonacci S, Mancuso S, Costanzo P, Oliverio M, Procopio A. Quali-quantitative monitoring of chemocatalytic cellulose conversion into lactic acid by FT-NIR spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 2021; 250:119367. [PMID: 33401184 DOI: 10.1016/j.saa.2020.119367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 12/15/2020] [Accepted: 12/18/2020] [Indexed: 06/12/2023]
Abstract
Chemocatalytic conversion of cellulose into lactic acid is a valuable alternative to simple sugar fermentation. Nevertheless, the procedures still need optimization to be translated to the industrial scale. Such translation would benefit by on-line monitoring of reaction parameters by fast, inexpensive, direct spectroscopic techniques. In this work, we propose the application of FT-NIR spectroscopy as a suitable analytical tool for monitoring the chemocatalytic conversion of cellulose into lactic acid. Comparison between different FT-NIR spectra at different reaction temperatures and times was exploited to qualitatively indicate the feasibility of the reaction. Besides, an FT-NIR prediction model was proposed for rapidly estimating the molar distribution of cellulose catalytic degradation components in the reaction mixtures. The calibration model was based on reference samples analysed by HPLC. The model was validated by an external validation set. Relevant statistical values of Ratio Performance to Deviations (RPD) referred to both calibration and external validation were obtained, thus demonstrating the potential of such analytical technique in process monitoring.
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Affiliation(s)
- Sofia Tallarico
- Dipartimento di Scienze della Salute, Università Magna Græcia di Catanzaro, Viele Europa - Campus Universitario S. Venuta - Loc, Germaneto, 88100 CZ, Italy
| | - Sonia Bonacci
- Dipartimento di Scienze della Salute, Università Magna Græcia di Catanzaro, Viele Europa - Campus Universitario S. Venuta - Loc, Germaneto, 88100 CZ, Italy
| | - Stefano Mancuso
- Dipartimento di Scienze della Salute, Università Magna Græcia di Catanzaro, Viele Europa - Campus Universitario S. Venuta - Loc, Germaneto, 88100 CZ, Italy
| | - Paola Costanzo
- Dipartimento di Scienze della Salute, Università Magna Græcia di Catanzaro, Viele Europa - Campus Universitario S. Venuta - Loc, Germaneto, 88100 CZ, Italy
| | - Manuela Oliverio
- Dipartimento di Scienze della Salute, Università Magna Græcia di Catanzaro, Viele Europa - Campus Universitario S. Venuta - Loc, Germaneto, 88100 CZ, Italy.
| | - Antonio Procopio
- Dipartimento di Scienze della Salute, Università Magna Græcia di Catanzaro, Viele Europa - Campus Universitario S. Venuta - Loc, Germaneto, 88100 CZ, Italy
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Temizkan R, Can A, Dogan MA, Mortas M, Ayvaz H. Rapid detection of milk fat adulteration in yoghurts using near and mid-infrared spectroscopy. Int Dairy J 2020. [DOI: 10.1016/j.idairyj.2020.104795] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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9
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Stocco G, Cipolat-Gotet C, Ferragina A, Berzaghi P, Bittante G. Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals. J Dairy Sci 2019; 102:9622-9638. [PMID: 31477307 DOI: 10.3168/jds.2019-16770] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 07/06/2019] [Indexed: 11/19/2022]
Abstract
Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100-2498, 850-1048, and 350-1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within instrument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.
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Affiliation(s)
- Giorgia Stocco
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy; Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy.
| | - Claudio Cipolat-Gotet
- Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Alessandro Ferragina
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Paolo Berzaghi
- Department of Animal Medicine, Production and Health (MAPS), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
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10
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11
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Li J, Zhang M, Dowell F, Wang D. Rapid Determination of Acetic Acid, Furfural, and 5-Hydroxymethylfurfural in Biomass Hydrolysates Using Near-Infrared Spectroscopy. ACS Omega 2018; 3:5355-5361. [PMID: 31458744 PMCID: PMC6642032 DOI: 10.1021/acsomega.8b00636] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 05/08/2018] [Indexed: 05/08/2023]
Abstract
Near-infrared spectroscopy (NIRS) is a rapid detection technique that has been used to characterize biomass. The objective of this study was to develop suitable NIRS models to predict the acetic acid, furfural, and 5-hydroxymethylfurfural (HMF) contents in biomass hydrolysates. Using a uniform distribution of pretreatment conditions, 60 representative biomass hydrolysates were prepared. Partial least-squares regression was used to develop models capable of predicting acetic acid, furfural, and HMF contents. Optimal models were built using the wavenumber range of 9000-8000 and 7000-5000 cm-1 with high R 2 for calibration and validation models, small root-mean-square error of calibration (<0.21) and root-mean-square error of prediction (RMSEP, <0.42), and a ratio of the standard deviation of the reference values to the RMSEP of >2.7. The NIRS method largely reduced the analytical time from ∼55 to <1 min and has no cost for reagents.
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Affiliation(s)
- Jun Li
- Department
of Biological and Agricultural Engineering and Department of
Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas 66506, United States
| | - Meng Zhang
- Department
of Biological and Agricultural Engineering and Department of
Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas 66506, United States
| | - Floyd Dowell
- Center
for Grain and Animal Health Research, USDA,
Agricultural Research Service, 1515 College Avenue, Manhattan, Kansas 66502, United States
- E-mail: . Tel.: 785-776-2753 (F.D.)
| | - Donghai Wang
- Department
of Biological and Agricultural Engineering and Department of
Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas 66506, United States
- E-mail: . Tel.: 785-532-2919. Fax: 785-532-5825 (D.W.)
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Currò S, Manuelian C, Penasa M, Cassandro M, De Marchi M. Technical note: Feasibility of near infrared transmittance spectroscopy to predict cheese ripeness. J Dairy Sci 2017; 100:8759-8763. [DOI: 10.3168/jds.2017-13001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2017] [Accepted: 08/02/2017] [Indexed: 11/19/2022]
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13
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Nunes CA, Souza VR, Rodrigues JF, Pinheiro ACM, Freitas MP, Bastos SC. Prediction of consumer acceptance in some thermoprocessed food by physical measurements and multivariate modeling. J FOOD PROCESS PRES 2017. [DOI: 10.1111/jfpp.13178] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Cleiton A. Nunes
- Department of Food Science; Federal University of Lavras, CP 3037; Lavras MG 37200-000 Brazil
| | - Vanessa R. Souza
- Department of Food Science; Federal University of Lavras, CP 3037; Lavras MG 37200-000 Brazil
| | - Jéssica F. Rodrigues
- Department of Food Science; Federal University of Lavras, CP 3037; Lavras MG 37200-000 Brazil
| | - Ana Carla M. Pinheiro
- Department of Food Science; Federal University of Lavras, CP 3037; Lavras MG 37200-000 Brazil
| | - Matheus P. Freitas
- Department of Chemistry; Federal University of Lavras, CP 3037; Lavras MG 37200-000 Brazil
| | - Sabrina C. Bastos
- Department of Nutrition; Federal University of Lavras, CP 3037; Lavras MG 37200-000 Brazil
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Nieto G, Xiong YL, Payne F, Castillo M. Light backscatter fiber optic sensor: a new tool for predicting the stability of pork emulsions containing antioxidative potato protein hydrolysate. Meat Sci 2015; 100:262-8. [PMID: 25460135 DOI: 10.1016/j.meatsci.2014.10.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 07/10/2014] [Accepted: 10/20/2014] [Indexed: 11/20/2022]
Abstract
The objective of this study was to determine whether light backscatter response from fresh pork meat emulsions is correlated to final product stability indices. A specially designed fiber optic measurement system was used in combination with a miniature fiber optic spectrometer to determine the intensity of light backscatter within the wavelength range 300-1100 nm (UV/VIS/NIR) at different radial distances (2, 2.5 and 3mm) with respect to the light source in pork meat emulsions with two fat levels (15%, 30%) and two levels (0, 2.5%) of the natural antioxidant hydrolyzed potato protein (HPP). Textural parameters (hardness, deformability, cohesiveness and breaking force), cooking loss, TBARS (1, 2, 3, and 7 days of storage at 4 °C) and CIELAB color coordinates of cooked emulsions were measured. The light backscatter was directly correlated with cooking losses, color, breaking force and TBARS. The optical configuration proposed would compensate for the emulsion heterogeneity, maximizing the existing correlation between the optical signal and the emulsion quality metrics.
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Barbin DF, Felicio ALDSM, Sun DW, Nixdorf SL, Hirooka EY. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Res Int 2014. [DOI: 10.1016/j.foodres.2014.01.005] [Citation(s) in RCA: 136] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Lamb A, Payne F, Xiong YL, Castillo M. Optical backscatter method for determining thermal denaturation of β-lactoglobulin and other whey proteins in milk. J Dairy Sci 2013; 96:1356-65. [DOI: 10.3168/jds.2012-5791] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2012] [Accepted: 12/03/2012] [Indexed: 11/19/2022]
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18
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RIBEIRO JULIANOS, SALVA TEREZINHAJ, FERREIRA MÁRCIAM. CHEMOMETRIC STUDIES FOR QUALITY CONTROL OF PROCESSED BRAZILIAN COFFEES USING DRIFTS. J FOOD QUALITY 2010. [DOI: 10.1111/j.1745-4557.2010.00309.x] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Kocaoglu-Vurma NA, Eliardi A, Drake MA, Rodriguez-Saona LE, Harper WJ. Rapid profiling of Swiss cheese by attenuated total reflectance (ATR) infrared spectroscopy and descriptive sensory analysis. J Food Sci 2010; 74:S232-9. [PMID: 19723228 DOI: 10.1111/j.1750-3841.2009.01188.x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The acceptability of cheese depends largely on the flavor formed during ripening. The flavor profiles of cheeses are complex and region- or manufacturer-specific which have made it challenging to understand the chemistry of flavor development and its correlation with sensory properties. Infrared spectroscopy is an attractive technology for the rapid, sensitive, and high-throughput analysis of foods, providing information related to its composition and conformation of food components from the spectra. Our objectives were to establish infrared spectral profiles to discriminate Swiss cheeses produced by different manufacturers in the United States and to develop predictive models for determination of sensory attributes based on infrared spectra. Fifteen samples from 3 Swiss cheese manufacturers were received and analyzed using attenuated total reflectance infrared spectroscopy (ATR-IR). The spectra were analyzed using soft independent modeling of class analogy (SIMCA) to build a classification model. The cheeses were profiled by a trained sensory panel using descriptive sensory analysis. The relationship between the descriptive sensory scores and ATR-IR spectra was assessed using partial least square regression (PLSR) analysis. SIMCA discriminated the Swiss cheeses based on manufacturer and production region. PLSR analysis generated prediction models with correlation coefficients of validation (rVal) between 0.69 and 0.96 with standard error of cross-validation (SECV) ranging from 0.04 to 0.29. Implementation of rapid infrared analysis by the Swiss cheese industry would help to streamline quality assurance.
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Affiliation(s)
- N A Kocaoglu-Vurma
- Dept. of Food Science and Technology, The Ohio State Univ., Columbus, OH 43210, USA
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Martín Del Campo ST, Bonnaire N, Picque D, Corrieu G. Initial studies into the characterisation of ripening stages of Emmental cheeses by mid-infrared spectroscopy. ACTA ACUST UNITED AC 2009. [DOI: 10.1051/dst/2008041] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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21
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Fagan C, Du CJ, O'Donnell C, Castillo M, Everard C, O'Callaghan D, Payne F. Application of Image Texture Analysis for Online Determination of Curd Moisture and Whey Solids in a Laboratory-Scale Stirred Cheese Vat. J Food Sci 2008; 73:E250-8. [DOI: 10.1111/j.1750-3841.2008.00814.x] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Woodcock T, Fagan CC, O’Donnell CP, Downey G. Application of Near and Mid-Infrared Spectroscopy to Determine Cheese Quality and Authenticity. FOOD BIOPROCESS TECH 2007. [DOI: 10.1007/s11947-007-0033-y] [Citation(s) in RCA: 110] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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