Energy profiling of dairy cows from routine milk mid-infrared analysis

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.     

Differentiation of saffron from four countries by mid-infrared spectroscopy and multivariate analysis

Mid-infrared spectroscopy combined with multivariate analysis has been applied for the discrimination of 250 saffron samples from Greece (40 samples), Iran (87 samples), Italy (60 samples) and Spain (63 samples). The infrared spectra of saffron filament samples and their organic extracts were recorded. Principal component analysis was applied to different spectral regions. Then the principal components were used as variables in discriminant analysis. The best discriminatory approach was achieved in the spectral region 2000–700 cm⁻¹. The spectral region responsible for the differentiation of Italian samples is in the region of carbonyl group around 1746 cm⁻¹. The spectral region at around 1600 cm⁻¹ and the band at 1670 cm⁻¹ are responsible for the differentiation of samples from the remaining countries. From the original grouped cases, 93.6% were correctly classified. The correct classification rates for saffron samples from Greece, Iran, Italy and Spain were 90.0, 89.5, 96.7 and 98.4%, respectively. The combination of infrared spectroscopic technique with multivariate analysis is a rapid and ambient method to discriminate saffron samples in terms of geographical origin.     

Predicting hydrolysis of whey protein by mid-infrared spectroscopy

The possibility of using mid-infrared spectroscopy for prediction of hydrolysis of whey protein was explored. Two different protein concentrations (5% and 8%) were trypsinated at different enzyme-to-substrate ratios (1:100 and 1:200) and followed over time (0–8 h). Degree of hydrolysis was determined by use of a fluorescamine assay, whereas size exclusion chromatography (SEC) was used to quantify the loss of intact protein. The results show spectral changes as hydrolysis proceeded over time. Increased absorbance intensities in the region from 1588 to 1540 cm⁻¹ as well as increased absorbance intensities around 1400 cm⁻¹ were observed with increasing hydrolysis. These changes were assigned to formation of primary amines as well as an increase in carboxyl groups as hydrolysis proceeded. As the level of free amino terminals correlates with spectral changes, infrared spectroscopy is a promising tool for prediction of degree of hydrolysis of whey proteins.     

中红外光谱技术在乳制品分析中的应用研究

介绍了中红外光谱技术的基本原理及化学计量学的相关知识,综合阐述了将中红外光谱技术与化学计量学方法结合应用于乳制品的成分分析、产地鉴别等方面研究的现状,并对其前景进行了展望。     

中红外光谱技术结合竞争性自适应重加权算法快速分析白酒风味组分

采用中红外光谱分析技术结合竞争性自适应重加权算法(CARS)对浓香型白酒基酒中的乳酸乙酯和乙酸乙酯的特征波长变量进行筛选后,建立偏最小二乘法(PLS)模型,并对其进行验证。结果表明,采用中红外光谱分析技术剔除明显噪声区域建立的PLS模型效果较好,而经CARS法进行特征波长选择后建立的CARS-PLS模型效果优于PLS模型,乙酸乙酯和乳酸乙酯的CARS-PLS模型相关系数R²分别为0.995、0.989,预测均方根误差(RMSEP)分别为12.80、4.54,相对分析误差(RPD)分别为8.78及8.60,模型经独立验证均取得了较高的预测精度,验证数据相关系数R²分别为0.994及0.992,RMSEP分别为13.55及4.86。该模型有较高的准确度及稳定性,能够用于白酒基酒中的乳酸乙酯和乙酸乙酯的快速分析,可为白酒酿造过程的质量把控提供技术方法。     

基于中红外光谱技术的羟丙基淀粉中掺假原淀粉的快速鉴别

采用中红外光谱获得羟丙基玉米淀粉中掺假原淀粉(玉米淀粉)的光谱数据,比较光谱数据的不同数学处理方法(求导阶数、平滑点数)对偏最小二乘法建立羟丙基玉米淀粉中掺假玉米淀粉预测模型的影响。当对光谱一阶求导和5点平滑时,模型校正集的决定系数(R_c~2)=0.984 0和均方根误差(RMSEC)=3.723%,羟丙基玉米淀粉中掺假水平实际值和所建立的模型预测值的相关系数为0.980 0。结果表明,中红外光谱法结合最小二乘法在羟丙基玉米淀粉掺假的快速鉴别上是可行的。     

近红外光谱、中红外光谱、拉曼光谱无损检测技术在食用油脂分析中的研究进展

近红外光谱(NIRS)、中红外光谱(MIRS)及拉曼光谱(Raman)技术能对样品进行无损分析,近年来在油脂分析领域表现出极大的应用潜力。本文综述了NIRS、MIRS及Raman技术在油脂分析中的研究进展,具体介绍了油脂脂肪酸组成分析、食用油的掺假鉴别、游离脂肪酸分析、脂肪酸顺反结构和不饱和度分析等方面的应用,并对NIRS、MIRS及Raman技术在油脂分析中的应用前景做了进一步展望。      

Phylogenetic and spectroscopic analysis of Alicyclobacillus isolates by 16S rDNA sequencing and mid-infrared spectroscopy

Alicyclobacillus spp. are a group of thermophilic, acidophilic, spore-forming bacteria, some of which cause spoilage in commercially pasteurized fruit and vegetable juice products including apple, orange, tomato and carrot juice. In this study, we characterized seven isolates of Alicyclobacillus (14-2, KF, A-Gala 2-1, C-Fuji 6, Gala 9-2, 1016, 18-1) and a reference strain A. acidiphilus DSM 14558 by a 16S rDNA sequencing technique and Fourier transform infrared (FT-IR) spectral features. The degree of similarity between Alicyclobacillus isolates based upon phylogenetic analysis of sequence data and multivariate statistical analysis of FT-IR spectral data was determined. Results from both methods were consistent, suggesting that a combination of methods targeting both 16S rDNA and mid-infrared spectroscopic signatures may be a suitable and more accurate approach to either identify or discriminate Alicyclobacillus isolates from fruit and vegetable juice products.     

茶香型苹果酒发酵特征及其动力学模型

以绿茶、苹果为原料,探讨茶香型苹果酒发酵过程中主要物质的变化规律,并基于Logistic模型和Luedekingpiret方程,对茶香型苹果酒发酵过程中酵母生长动力学、酒精生成动力学、基质消耗动力学模型进行模拟。实验结果显示:模型预测值和实测值的拟合度分别为0.9679、0.9765、0.9849,模型拟合良好,较好的反映了茶香型苹果酒发酵过程的动力学特征。发酵10d后茶香型苹果酒的酒精度为10.2%(v/v),残余总糖为6.501g/L,总多酚为5.07mg/mL,总酯为0.251g/L,pH为3.54,酒体呈黄褐色,晶亮,具有新鲜悦怡的酒香和优雅的绿茶香。      

Determination of the botanical origin of honey by Fourier-transformed infrared spectroscopy: an approach for routine analysis

Fourier-transformed infrared spectroscopy has been established for analysing most of the physical–chemical parameters of honey. Additionally this technique can be used for determination of the botanical origin of a honey sample by comparison of the mid-infrared spectra. In this study calibrations for authentification of the main regional honey types should be developed to be able to measure simultaneously physical–chemical properties and the botanical origin at a minimum of time and at low costs. Honey samples from local beekeepers were collected and characterized by standard methods. The most common honey types were used for calibration. We recorded the mid infrared spectrum from each honey sample. Classification models were achieved by PCA-calibration and validated with samples from various botanical origins. Honey of Brassica spp., Robinia pseudoacacia, Calluna vulgaris, Trifolium spp., Tilia spp., Helianthus annuus, Centaurea cyanus and honeydew honey was used for calibration. Afterwards the calibration models were improved during routine analysis. Most of the honey samples from rape, false acacia, heather and honeydew can be classified correctly by the FTIR in consideration of the physical–chemical and sensorial properties.     

Effectiveness of mid-infrared spectroscopy for the prediction of cow milk metabolites

Proton nuclear magnetic resonance (¹H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of ¹H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through ¹H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional ¹H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.     

Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS)

Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) and multivariate statistical analysis methods were used for the identification and classification of honey from different floral sources. The 82 honey samples (robinia, chestnut, citrus, polyfloral) were scanned by DRIFTS in the region 4000–600 cm⁻¹ and also transformed in 1st and 2nd derivatives. Spectral data were analyzed by principal component analysis, general discriminant analysis and classification tree analysis. Classification accuracy near 100% was obtained by discriminant and classification tree analyses. Classification models were successfully validated with one-third leave out method and a classification of about 100% were achieved.     

Prediction of sugars and acids in Chinese rice wine by mid-infrared spectroscopy

The use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the rapid determination of sugars and acids in Chinese rice wine was presented in this study. Calibration models were developed by partial least squares regression (PLSR) for eleven parameters related to sugar content and acidity—namely, total sugar, non-sugar solid, glucose, maltose, isomaltotriose, isomaltose, panose, total acid, amino acid nitrogen, pH and lactic acid. In the calibration step, most of the parameters were accurately determined, obtaining regression coefficients of calibration (r_cal) ranging from 0.821 to 0.991. In validation, regression coefficients of validation (r_val) obtained for most parameters were higher than 0.85. Unsatisfactory predictions were obtained for isomaltotriose and isomaltose with r_val being 0.488 and 0.716, respectively. The residual predictive deviation (RPD) values were also higher than or close to 2.0 for all the parameters except for isomaltose and isomaltotriose. Overall, the results indicate that MIR spectroscopy can be applied to the quality determination of Chinese rice wine.     

Classification of simple and complex sugar adulterants in honey by mid-infrared spectroscopy

Adulteration of honey with sugars is the most crucial quality assurance concern to the honey industry. The application of Fourier transform infrared spectroscopy as a screening tool for the determination of the type of sugar adulterant in honey was investigated. Spectra of honey adulterated with simple and complex sugars were recorded in the mid-infrared range using the attenuated total reflectance accessory of a Fourier transform infrared spectrometer. Adulterants considered were sugars (glucose, fructose and sucrose) and invert sugars (cane invert and beet invert). Predictive models were developed to classify the adulterated honey samples using discriminant analysis. Spectral data were compressed using principal component analysis and partial least-square methods. Linear discriminant analysis was used to discriminate the type of adulterant in three different honey varieties. An optimum classification of 100% was achieved for honey samples adulterated with glucose, fructose, sucrose and beet and cane invert sugars. Results demonstrated that discriminant analysis of the spectra of adulterated honey samples could be used for rapid detection of adulteration in honey.     

中红外光谱在食品掺假检测中的应用

中红外光谱 (MIR)以其在 40 0 0 - 40 0cm-1处的指纹吸收特性广泛应用于机电、医药等领域。随着计算机技术和化学计量学方法的发展 ,MIR在食品领域的应用研究也已展开。本文仅就其在食品掺假检测中的应用加以概述     

Strategies for detecting organic liquids on soils using mid-infrared reflection spectroscopy

Stand-off monitoring for chemical spills can provide timely information for cleanup efforts, and mid-infrared reflection spectroscopy is one approach being investigated for spill detection. Using laboratory data, anomaly and target detection strategies were examined for the detection of four different low-volatility organic liquids on two different soil types. Several preprocessing and signal-weighting strategies were studied. Anomaly detection for C-H bands was good using second-derivative preprocessing and provided similar performance to that of target detection approaches such as generalized least-squares and partial least-squares, with detections at soil loads of approximately 3-6 microg/cm2 a real dosage. Good performance was also found for the detection of P=O, O-H, and C=O stretching vibrational modes, but the optimal strategy varied. The simplicity and generality of anomaly detection is attractive; however, target detection provides more capability for classification.     

Evaluation of chemical parameters in soft mold-ripened cheese during ripening by mid-infrared spectroscopy

The suitability of mid-infrared spectroscopy (MIR) to follow the evolution throughout ripening of specific physicochemical parameters in Camembert-type cheeses was evaluated. The infrared spectra were obtained directly from raw cheese samples deposited on an attenuated total reflectance crystal. Significant correlations were observed between physicochemical data, pH, acid-soluble nitrogen, nonprotein nitrogen, ammonia (NH₄⁺), lactose, and lactic acid. Dry matter showed significant correlation only with lactose and nonprotein nitrogen. Principal components analysis factorial maps of physicochemical data showed a ripening evolution in 2 steps, from d 1 to d 7 and from d 8 to d 27, similar to that observed previously from infrared spectral data. Partial least squares regressions made it possible to obtain good prediction models for dry matter, acid-soluble nitrogen, nonprotein nitrogen, lactose, lactic acid, and NH₄⁺ values from spectral data of raw cheese. The values of 3 statistical parameters (coefficient of determination, root mean square error of cross validation, and ratio prediction deviation) are satisfactory. Less precise models were obtained for pH.     

Development of Fourier-transformed mid-infrared spectroscopy prediction models for major constituents of fractions of delactosated,defatted milk obtained through ultra- and nanofiltration

Milk filtration procedures are gaining relevance in the dairy industry because milk ultra- and nanofiltrates are used to increase milk processing efficiency, and as additives for products with improved nutraceutical properties. This study aimed to develop Fourier-transformed mid-infrared spectroscopy calibrations for ultra- and nanopermeate and retentate fractions of defatted and delactosated milk. A total of 154 samples from different milk fractions were collected and analyzed using reference methods to determine protein, solids-not-fat, glucose, and galactose content. The obtained values were matched with their respective Fourier-transformed mid-infrared spectroscopy spectra to develop new prediction models. Calibrations for each trait were built following 3 different approaches to get the best prediction models: (1) using the entire data set, (2) using 3 subsets based on component concentrations (level approach), and (3) using hierarchical clusters calculated with pairwise Mahalanobis distance among spectra (cluster approach). Calibrations were developed using partial least squares regression, after removing low signal-to-noise ratio wavelengths, and validated through a leave-one-out cross-validation procedure. In addition, the accuracy of the predicted values within each fraction was checked for each approach. Dividing the data set into subsets improved prediction models for each trait and for the samples in each milk fraction. Without considering milk fraction, the best improvement was observed for glucose and galactose. Glucose ratio performance deviation in cross-validation (RPD) increased from 7.42 to 11.31 and 11.06, for cluster and level approaches, respectively, whereas galactose RPD increased from 8.86 to 11.69 and 11.27 for cluster and level approaches, respectively. Considering milk fractions, the best improvement was observed for protein content, where RPD ranged from 0.08 to 6.06 for the whole data set calibration, whereas it ranged from 0.43 to 40.34 for the subset calibration approaches. Cluster and level approaches to build calibration models were comparable for samples from different fractions, suggesting that the 2 subsetting protocols should be both investigated to get the best prediction performances.     

Prediction of sensory score of Italian traditional balsamic vinegars of Reggio-Emilia by mid-infrared spectroscopy

In this study the potential of Fourier Transform Mid-Infrared (FT-MIR) spectroscopy to predict the sensory score of traditional balsamic vinegar (TBV) of Reggio-Emilia was investigated. The composition of two hundred commercial TBV samples was analysed and the sensory scores, ranging from 133 to 306 points, were evaluated by a certified panel of master experts (reference method). Partial least squares (PLS) regression, obtained from selected pre-processing signal techniques, was used for multivariate calibration to relate the sensory score to the MIR spectra. Performance of different models was compared in terms of coefficient of correlation (r) and root mean square error of cross-validation (RMSECV). The overall best prediction results were obtained using second order derivative with autoscaling and mean-centering of spectral data with the correlation coefficient of 0.889 and 0.885, respectively. It was concluded that the MIR spectroscopy is suitable for rapid instrumental screening of TBV sensory quality.