1
|
Du C, Zhao X, Chu C, Nan L, Ren X, Yan L, Zhang X, Zhang S, Teng Z. Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 324:124969. [PMID: 39153347 DOI: 10.1016/j.saa.2024.124969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 08/06/2024] [Accepted: 08/11/2024] [Indexed: 08/19/2024]
Abstract
The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.
Collapse
Affiliation(s)
- Chao Du
- College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - XueHan Zhao
- College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Chu Chu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | | | - XiaoLi Ren
- Henan Dairy Herd Improvement Center, Zhengzhou 450000, China
| | - Lei Yan
- Henan Dairy Herd Improvement Center, Zhengzhou 450000, China
| | - XiaoJian Zhang
- College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - ShuJun Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - ZhanWei Teng
- College of Animal Science and Veterinary Medicine, Henan Institute of Science and Technology, Xinxiang 453003, China.
| |
Collapse
|
2
|
Liu H, Liu H, Li J, Wang Y. Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion. PHYTOCHEMICAL ANALYSIS : PCA 2024. [PMID: 38937551 DOI: 10.1002/pca.3413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/13/2024] [Accepted: 06/16/2024] [Indexed: 06/29/2024]
Abstract
INTRODUCTION Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI. OBJECTIVE Realization of rapid and accurate identification of the origin of G. elata BI. MATERIALS AND METHODS Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI. SAMPLES Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images. RESULTS The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness. CONCLUSION Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.
Collapse
Affiliation(s)
- Hong Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Honggao Liu
- Yunnan Key Laboratory of Gastrodia and Fungi Symbiotic Biology, Zhaotong University, Zhaotong, Yunnan, China
| | - Jieqing Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| |
Collapse
|
3
|
Guerra A, Boselli C, Galli T, Ciofi L, Fichi G, Marchi MD, Manuelian CL. Low Effectiveness of Mid-Infrared Spectroscopy Prediction Models of Mediterranean Italian Buffalo Bulk Milk Coagulation Traits. Foods 2024; 13:1957. [PMID: 38998463 PMCID: PMC11241227 DOI: 10.3390/foods13131957] [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: 05/26/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
This study evaluated the potential use of mid-infrared spectroscopy to predict milk coagulation traits in bulk milk from Mediterranean Italian buffaloes. A total of 1736 bulk milk samples from 55 farms in central Italy were collected during the official milk quality testing system. The prediction models were developed based on modified partial least-squares regression with 75% of the samples and validated with the remaining samples. All bulk milk samples coagulated between 7.37 and 29.45 min. Average values for milk coagulation traits in the calibration set were 17.71 min, 3.29 min, and 38.83 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. The validation set included samples with similar mean and standard deviation for each trait. The prediction models showed the greatest coefficient of determination of external validation (0.57) and the ratio of prediction to deviation (1.52) for curd firmness. Similar fitting statistics of the prediction models were obtained for rennet coagulation time and curd firming time. In conclusion, the prediction models for all three coagulation traits were below the threshold to consider the prediction models adequate even for rough screening of the samples.
Collapse
Affiliation(s)
- Alberto Guerra
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Carlo Boselli
- Experimental Zooprophylactic Institute Lazio and Toscana "Mariano Aleandri", Via Appia Nuova 1411, 00178 Rome, Italy
| | - Tiziana Galli
- Experimental Zooprophylactic Institute Lazio and Toscana "Mariano Aleandri", Via Appia Nuova 1411, 00178 Rome, Italy
| | - Letizia Ciofi
- Experimental Zooprophylactic Institute Lazio and Toscana "Mariano Aleandri", Via Appia Nuova 1411, 00178 Rome, Italy
| | - GianLuca Fichi
- Experimental Zooprophylactic Institute Lazio and Toscana "Mariano Aleandri", Via Appia Nuova 1411, 00178 Rome, Italy
| | - Massimo De Marchi
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Carmen L Manuelian
- Group de Recerca en Remugats (G2R), Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| |
Collapse
|
4
|
Chu C, Wang H, Luo X, Wen P, Nan L, Du C, Fan Y, Gao D, Wang D, Yang Z, Yang G, Liu L, Li Y, Hu B, Abula Z, Zhang S. Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods. Foods 2023; 12:3856. [PMID: 37893749 PMCID: PMC10606090 DOI: 10.3390/foods12203856] [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: 09/21/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.
Collapse
Affiliation(s)
- Chu Chu
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Haitong Wang
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Xuelu Luo
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Peipei Wen
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Liangkang Nan
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Chao Du
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Yikai Fan
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Dengying Gao
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Dongwei Wang
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Zhuo Yang
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Guochang Yang
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Li Liu
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Yongqing Li
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| | - Bo Hu
- Quality Standards Institue of Animal Husbandry, Xinjiang Academy of Animal Science, Urumqi 830012, China; (B.H.); (Z.A.)
| | - Zunongjiang Abula
- Quality Standards Institue of Animal Husbandry, Xinjiang Academy of Animal Science, Urumqi 830012, China; (B.H.); (Z.A.)
| | - Shujun Zhang
- Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China; (C.C.); (H.W.); (X.L.); (P.W.); (L.N.); (C.D.); (Y.F.); (D.W.); (Z.Y.); (G.Y.); (L.L.); (Y.L.)
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
| |
Collapse
|
5
|
Ceniti C, Spina AA, Piras C, Oppedisano F, Tilocca B, Roncada P, Britti D, Morittu VM. Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy. Foods 2023; 12:2917. [PMID: 37569186 PMCID: PMC10418805 DOI: 10.3390/foods12152917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.
Collapse
Affiliation(s)
- Carlotta Ceniti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Anna Antonella Spina
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Cristian Piras
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Francesca Oppedisano
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Bruno Tilocca
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Paola Roncada
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Domenico Britti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
| | - Valeria Maria Morittu
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
| |
Collapse
|
6
|
Tian H, Xiong J, Chen S, Yu H, Chen C, Huang J, Yuan H, Lou X. Rapid identification of adulteration in raw bovine milk with soymilk by electronic nose and headspace-gas chromatography ion-mobility spectrometry. Food Chem X 2023; 18:100696. [PMID: 37187488 PMCID: PMC10176159 DOI: 10.1016/j.fochx.2023.100696] [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: 01/27/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
The adulteration of soymilk (SM) into raw bovine milk (RM) to gain profit without declaration could cause a health risk. In this study, electronic nose (E-nose) and headspace-gas chromatography ion-mobility spectrometry (HS-GC-IMS) were applied to establish a rapid and effective method to identify adulteration in RM with SM. The obtained data from HS-GC-IMS and E-nose can distinguish the adulterated samples with SM by principal component analysis. Furthermore, a quantitative model of partial least squares was established. The detection limits of E-nose and HS-GC-IMS quantitative models were 1.53% and 1.43%, the root mean square errors of prediction were 0.7390 and 0.5621, the determination coefficients of prediction were 0.9940 and 0.9958, and the relative percentage difference were 10.02 and 13.27, respectively, indicating quantitative regression and good prediction performances of SM adulteration levels in RM were achieved. This research can provide scientific information on the rapid, non-destructive and effective adulteration detection for RM.
Collapse
|
7
|
Villar-Hernández BDJ, Amalfitano N, Cecchinato A, Pazzola M, Vacca GM, Bittante G. Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats. Foods 2023; 12:foods12040807. [PMID: 36832882 PMCID: PMC9955890 DOI: 10.3390/foods12040807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm-1), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk.
Collapse
Affiliation(s)
| | - Nicolò Amalfitano
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
| | - Michele Pazzola
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | | | - Giovanni Bittante
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
- Correspondence:
| |
Collapse
|
8
|
Samal RR, Sundaray K, Tulsiyan KD, Saha S, Chainy GBN, Subudhi U. Compromised conformation and kinetics of catalase in the presence of propylthiouracil: A biophysical study and alleviation by curcumin. Int J Biol Macromol 2023; 226:1547-1559. [PMID: 36455824 DOI: 10.1016/j.ijbiomac.2022.11.266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
In the present study, the inhibitory effect of propylthiouracil (PTU) on bovine liver catalase (BLC) activity was studied in the presence of curcumin (CUR). The results suggest that the PTU-induced decrease in BLC activity was caused by a change in conformation of BLC with reduced α-helical content and decrease in zeta potential. Nevertheless, temperature-dependent activation of CUR protects the activity of BLC by restoring the secondary conformation and zeta potential of BLC. CUR inhibited the time-induced reduction in BLC activity and the protection was increased with increasing concentrations of CUR and found to be significant even from 1:0.1 molar ratios. The enzyme kinetics confirmed the high catalytic efficiency of BLC in presence of CUR than PTU. The protective role of CUR was due to the formation of a more stabilized complex as demonstrated by molecular docking, and fourier-transform infrared study. Isothermal titration calorimetric study supports for a favourable reaction between BLC and PTU or CUR due to the negative ΔH, and positive TΔS. Although the number of binding sites for PTU and CUR was found to be 10 and 7, respectively, the binding affinity between CUR and BLC is approximately 3.72 fold stronger than BLC-PTU complex. The increased melting temperature of BLC was noticed in presence of CUR suggesting the protective potential of CUR towards biomolecules. Indeed, this is the first biophysical study to describe the molecular mechanism of PTU-induced reduction in BLC activity and alleviation by CUR with detail kinetics. Thus, CUR can be further extended to other antioxidant enzymes or compromised biomolecules for therapeutic interventions.
Collapse
Affiliation(s)
- Rashmi R Samal
- Biochemistry & Biophysics Laboratory, CSIR-Institute of Minerals & Materials Technology, Bhubaneswar 751013, Odisha, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Kajal Sundaray
- Biochemistry & Biophysics Laboratory, CSIR-Institute of Minerals & Materials Technology, Bhubaneswar 751013, Odisha, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Kiran D Tulsiyan
- School of Chemical Sciences, National Institute of Science Education & Research, Bhubaneswar 752050, Odisha, India; Homi Bhaba National Institute, Mumbai 400094, India
| | - Sumit Saha
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India; Materials Chemistry Department, CSIR-Institute of Minerals & Materials Technology, Bhubaneswar 751013, Odisha, India
| | - Gagan B N Chainy
- Department of Biotechnology, Utkal University, Bhubaneswar 751004, Odisha, India
| | - Umakanta Subudhi
- Biochemistry & Biophysics Laboratory, CSIR-Institute of Minerals & Materials Technology, Bhubaneswar 751013, Odisha, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India.
| |
Collapse
|
9
|
Spina AA, Ceniti C, Piras C, Tilocca B, Britti D, Morittu VM. Mid-Infrared (MIR) Spectroscopy for the quantitative detection of cow’s milk in buffalo milk. JOURNAL OF ANIMAL SCIENCE AND TECHNOLOGY 2022; 64:531-538. [PMID: 35709130 PMCID: PMC9184705 DOI: 10.5187/jast.2022.e22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/12/2022] [Accepted: 04/01/2022] [Indexed: 11/27/2022]
Abstract
In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The
fraudulent adulteration of buffalo milk with cheaper and more available milk of
other species is very frequent. In the present study, Fourier transform infrared
spectroscopy (FTIR), in combination with multivariate analysis by partial least
square (PLS) regression, was applied to quantitatively detect the adulteration
of buffalo milk with cow milk by using a fully automatic equipment dedicated to
the routine analysis of the milk composition. To enhance the heterogeneity, cow
and buffalo bulk milk was collected for a period of over three years from
different dairy farms. A total of 119 samples were used for the analysis to
generate 17 different concentrations of buffalo-cow milk mixtures. This
procedure was used to enhance variability and to properly randomize the trials.
The obtained calibration model showed an R2 ≥
0.99 (R2cal. = 0.99861; root mean square error of
cross-validation [RMSEC] = 2.04; R2val. = 0.99803;
root mean square error of prediction [RMSEP] = 2.84; root mean square error of
cross-validation [RMSECV] = 2.44) suggesting that this method could be
successfully applied in the routine analysis of buffalo milk composition,
providing rapid screening for possible adulteration with cow’s milk at no
additional cost.
Collapse
Affiliation(s)
- Anna Antonella Spina
- Interdepartmental Services Centre of
Veterinary for Human and Animal Health, Department of Health Science, Magna
Græcia University, Catanzaro 88100, Italy
- Corresponding author: Anna Antonella Spina,
Interdepartmental Services Centre of Veterinary for Human and Animal Health,
Department of Health Science, Magna Græcia University, Catanzaro 88100,
Italy. Tel: +39-0961-3694146, E-mail:
| | - Carlotta Ceniti
- Interdepartmental Services Centre of
Veterinary for Human and Animal Health, Department of Health Science, Magna
Græcia University, Catanzaro 88100, Italy
- Corresponding author: Carlotta Ceniti,
Interdepartmental Services Centre of Veterinary for Human and Animal Health,
Department of Health Science, Magna Græcia University, Catanzaro 88100,
Italy. Tel: +39-0961-3694146, E-mail:
| | - Cristian Piras
- Interdepartmental Services Centre of
Veterinary for Human and Animal Health, Department of Health Science, Magna
Græcia University, Catanzaro 88100, Italy
| | - Bruno Tilocca
- Interdepartmental Services Centre of
Veterinary for Human and Animal Health, Department of Health Science, Magna
Græcia University, Catanzaro 88100, Italy
| | - Domenico Britti
- Interdepartmental Services Centre of
Veterinary for Human and Animal Health, Department of Health Science, Magna
Græcia University, Catanzaro 88100, Italy
| | - Valeria Maria Morittu
- Interdepartmental Services Centre of
Veterinary for Human and Animal Health, Department of Health Science, Magna
Græcia University, Catanzaro 88100, Italy
| |
Collapse
|
10
|
Development of an embedded system for real-time milk spoilage monitoring and adulteration detection. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2021.105207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
11
|
Augusto da Costa Filho P, Chen Y, Cavin C, Galluzzo R. Mid-infrared spectroscopy: Screening method for analysis of food adulterants in reconstituted skimmed milk powder. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
|