A comparison and joint use of NIR and MIR spectroscopic methods for the determination of some parameters in European Emmental cheese

The determination of winter cheese chemical properties, namely, fat, sodium chloride (NaCl), pH, non protein nitrogen (NPN), total nitrogen (TN) and water soluble nitrogen (WSN) was done using spectroscopic technologies with different wavelength zones. The Emmental cheeses provided from different European countries were studied. A total of 91 cheeses produced during the winter time in Austria (n=4), Finland (n=6), Germany (n=13), France (n=30) and Switzerland (n=38) were analysed by near infrared (NIR) and mid infrared (MIR) spectroscopies. The combination of these two spectral regions (sum of their spectra) was also studied. The partial least square (PLS) regression with the leave one-out cross validation technique was used to build up calibration models using data set designated as calibration set. These models were validated with another data set designated as validation set. The obtained results suggest the use of the NIR for the determination of fat and TN contents, and the MIR for NaCl and NPN contents as well as for the pH. Similar results were obtained for WSN using the two techniques together. The combined spectra of both NIR and MIR did improve the results, while providing comparable results to those obtained from either the NIR or MIR spectroscopy.     

A comparison and joint use of VIS-NIR and MIR spectroscopic methods for the determination of some chemical parameters in soft cheeses at external and central zones: a preliminary study

There is a strong tendency towards exploring rapid and low cost methods for determining chemical parameters and degree of the ripening of cheeses. The visible-near infrared (VIS-NIR), mid infrared (MIR) and combination of VIS-NIR and MIR spectroscopic methods for measurements of some selected parameters of soft cheeses were compared. Fifteen traditional and stabilised retail soft cheeses, differing in manufacturing process were studied. Fat, dry matter (DM), pH, total nitrogen (TN) and water soluble nitrogen (WSN) contents were determined by reference methods and scanned with VIS-NIR and MIR spectrophotometers in reflectance mode. Three separate prediction models were developed from the VIS-NIR, MIR and the joint VIS-NIR-MIR spectra using the partial least square (PLS) regression and leave one-out cross-validation technique. Results showed that fat, DM, TN and WSN were the best predicted with the VIS-NIR models providing the lowest values of the root mean square error of prediction (RMSEP) of 1.32, 0.70, 0.11 and 0.10, respectively. The combination of the VIS-NIR and MIR spectral improved slightly the prediction of only the pH. This suggests using the VIS-NIR for the determination of fat, DM, TN and WSN. The pH can also be predicted from the two techniques with approximate quantitative prediction, while a difference between low and high levels of WSN/TN ratio could be determined by the VIS-NIR, MIR or joint use of VIS-NIR-MIR.     

Prediction of sensory attributes of European Emmental cheese using near-infrared spectroscopy: A feasibility study

The present work evaluated the ability of near infrared (NIR) spectroscopy in predicting some sensory attributes of 20 Emmental cheeses originating from different European regions. For the purpose of this study four appearance and texture attributes, namely, adhesivity, friability, elasticity and firmness and six olfacto-gustatory attributes namely, aroma intensity, odour intensity, bitterness, saltiness, acidity and sweetness were selected by the sensory panel. Calibration models between sensory properties and NIR spectra were developed using partial least squares (PLS) regression. The squared correlation coefficients (R²) were greater than 0.5 for adhesivity, elasticity, firmness, aroma, bitterness, saltiness, acidity and sweetness. In addition, a good correlation between sensory attributes and NIR spectra was found using canonical correlation analysis (CCA). Therefore, this work demonstrates the feasibility of NIR to predict some sensory attributes since a relatively high correlation between sensory data and NIR spectra was found. However, further research with a large data bases will be needed in order to validate the method.     

Determining the geographic origin of Emmental cheeses produced during winter and summer using a technique based on the concatenation of MIR and fluorescence spectroscopic data

A technique that used multivariate data analysis to combine mid-infrared (MIR) spectroscopy with front-face fluorescence spectroscopy was used to discriminate between Emmental cheeses originating from different European countries: Austria (n=12), Finland (n=10), Germany (n=19), France (n=57), and Switzerland (n=65). In total, 163 Emmental cheeses produced in winter (n=91) and summer (n=72) periods were investigated. When Factorial Discriminant Analysis was applied to either the infrared or fluorescence spectral data the classifications were not satisfactory. Therefore, the first twenty principal components (PCs) of the PCA extracted from each data set (MIR and tryptophan fluorescence spectra) were pooled (concatenated) into a single matrix and analysed by Factorial Discriminant Analysis. Correct classifications were obtained for the samples for 89% of the calibration spectra and 76.7% of the validation spectra. The discrimination for cheeses from Finland was excellent, while Austrian, German, French and Swiss cheeses were also discriminated well although a few samples were misclassified. It was concluded that concatenation of the data from the two spectroscopic techniques is an efficient technique for authenticating Emmental cheeses independently of their manufacturing period.     

Prediction of colour of European Emmental cheeses by using near infrared spectroscopy: a feasibility study

Near infrared (NIR) spectroscopy was used to predict colour of European Emmental cheese samples. Colour values (L, a and b) were measured on 20 Emmental cheese samples using a Hunter-lab D25-D-2 optical head in the system according to Hunter to determine L (brightness), a (green-red component) and b (blue-yellow component). The diffuse reflectance of the investigated cheeses was also determined by a Büchi NIR Lab N-200 spectrometer using a rotating measuring cell in the range of 1000–2500 nm. The best results for L-value (squared correlation coefficient (R ²) = 0.56, root mean square error of cross-validation (RMSECV) = 0.76, ratio of prediction deviation (RPD) = 1.89 and range error ratio (RER) = 7.91), a-value (R ² = 0.72, RMSECV = 0.15, RPD = 1.98 and RER = 7.6) and b-value (R ² = 0.82, RMSECV = 0.52, RPD = 2.56 and RER = 9.42) were obtained when the first 12 principal components (PCs) of the principal component analysis (PCA) applied on normalised NIR spectra were used. It can be concluded that NIR spectroscopy could be used to predict b-value. The a- and L-values can also be predicted from NIR technique with approximate quantitative prediction.     

Prediction of the rheology parameters of ripened semi-hard cheeses using fluorescence spectra in the UV and visible ranges recorded at a young stage

Storage modulus (G′), loss modulus (G″), strain, tan (δ) and complex viscosity (η*) of 20 semi-hard cheeses were measured by dynamic oscillatory analysis after 2, 30 and 60 days of ripening. On the same cheeses and at the same ages, tryptophan and riboflavin fluorescence spectra were recorded. The aim was to predict the rheology parameters of ripened cheeses from spectra recorded on these cheeses at a young stage. Using partial least square, tryptophan fluorescence spectra recorded at 20 °C on 2-days-old cheeses predicted G′, G″, strain, tan (δ) and η* measured at 80 °C on the 60-days-old cheeses with correlation coefficients (R) of 0.98, 0.97, 0.98, 0.98 and 0.97, respectively. Riboflavin fluorescence spectra gave slightly lower correlation coefficients of 0.88, 0.88, 0.92, 0.87 and 0.88, respectively. Dependent only on visible light, the riboflavin fluorescence spectra potentially provide viable and economic prediction of the rheology of ripe cheese.     

Utilisation of front face fluorescence spectroscopy as a tool for the prediction of some chemical parameters and the melting point of semi-hard and hard cheeses: a preliminary study

The objective of this preliminary study was to evaluate the usefulness of front face fluorescence spectroscopy to predict some chemical parameters [pH, fat, dry matter (DM), fat in DM, total nitrogen (TN) and water soluble nitrogen (WSN)] and cheese meltability of semi-hard and hard cheeses. Dynamic testing rheology was used to determine the melting point of cheeses corresponding to the temperature at which loss tangent (tan δ) = 1. Tryptophan and vitamin A fluorescence spectra were, also, recorded on cheese samples at 20 °C. The partial least squares (PLS) regression with the leave one-out cross-validation technique was used to build up calibration models. Excellent predictions were obtained from the tryptophan and vitamin A models for fat (R ² = 0.99 and 0.97, respectively), DM (R ² = 0.94 and 0.96, respectively), fat in DM (R ² = 0.92 and 0.99, respectively), TN (R ² = 0.91 and 0.91, respectively). Excellent predictions were also obtained for WSN (R ² = 0.96) and melting point (R ² = 0.97) from vitamin A spectra, while only good predictions for these two parameters (R ² = 0.90 and R ² = 0.87, respectively) were obtained from tryptophan spectra. The results for pH were good (R ² = 0.82) and approximate (R ² = 0.76) with tryptophan and vitamin A, respectively.     

近红外光谱分析法快速测定稻谷常规化学指标

采集150份有代表性的我国南方地区稻谷样品的近红外光谱,用偏最小二回归分析法(PLS),建立了稻谷的水分、直链淀粉、蛋白以及胶稠度的近红外定量分析模型,并对30份预测集样品进行了验证。水分、直链淀粉、蛋白以及胶稠度的校正集模型的决定系数所(R2)分别为0.990 3、0.560 3、0.913 2以及0.678 0,交互验证均方根误差(RMSECV)分别为0.372 8%、1.456 9%、0.305 4%以及5.031 5%;验证集标准预测偏差(RMSEP)分别为0.382 5%、1.465 0%、0.510 0%以及5.052 1%。结果表明,近红外光谱分析法可以满足快速分析的要求。     

Application of synchronous fluorescence spectroscopy for the determination of some chemical parameters in PDO French blue cheeses

The present study was aimed at investigating the potential of using synchronous fluorescence spectroscopy (SFS) coupled with multivariate statistical analyses for the determination of some chemical parameters (pH, fat, dry matter, protein and soluble nitrogen) of French blue-veined cheeses belonging to four brands (FA-Fourme d’Ambert, FM-Fourme de Montbrison, BA-Bleu d’Auvergne and BC-Bleu des Causses). Three partial least square regression models with leave-one-out cross-validation technique were considered in the present study. The first one including the “Fourme cheeses” (FA and FM), the second one including the “Blue cheeses” (BA and BC) and the last one including the 4 Blue-veined cheeses (FA, FM, BA and BC). The models qualities were investigated principally by the R ² (coefficient of determination) and the RPD (ratio of standard deviation to root-mean-square error of cross-validation) factors. The results showed that SFS succeeded to predict ash and protein in Blue (ash: R ² = 0.90, RPD = 3.17; protein R ² = 0.80, RPD = 2.24) or Fourme cheeses (ash: R ² = 0.81, RPD = 2.29; protein R ² = 0.81, RPD = 2.26) when considered individually, while SFS failed to predict all the physicochemical parameters when the two groups were analyzed jointly.     

MIR spectroscopy and partial least-squares regression for determination of phospholipids in rapeseed oils at various stages of technological process

Fourier transform-mid infrared spectroscopy (FT-MIR) and partial least-square (PLS) regression were used for determination of phospholipids (PL) in rapeseed oils at various stages of technological process. The standard error of calibration (SEC) and the standard error of prediction (SEP) were calculated for evaluation of the calibration models. The chemometric calibration model was prepared in spectral region 1760–860 cm⁻¹ for standard PL solutions (1.5–120 mg/mL). Obtained mean concentrations of PL in rapeseed oils at different stages of conventional technological operations varied from 22,710 to 224.6 mg/kg. Satisfactory values of precision (RSD = 0.23–0.73%) and accuracy (recovery – 96.1–101.9%), demonstrate the benefit of the proposed MIR-PLS method in the routine analysis of PL in vegetable oils.     

Classification of Pecorino cheeses produced in Italy according to their ripening time and manufacturing technique using Fourier transform infrared spectroscopy

Fourier-transform infrared spectroscopy, followed by linear discriminant analysis of the spectral data, was used to classify Italian Pecorino cheeses according to their ripening time and manufacturing technique. The Fourier transform infrared spectra of the cheeses were divided into 18 regions and the normalized absorbance peak areas within these regions were used as predictors. Linear discriminant analysis models were constructed to classify Pecorino cheeses according to different ripening stages (hard and semi-hard) or according to their manufacturing technique (fossa and nonfossa cheeses). An excellent resolution was achieved according to both ripening time and manufacturing technique. Also, a final linear discriminant analysis model considering the 3 categories (hard nonfossa, hard fossa, and semi-hard nonfossa) was constructed. A good resolution among the 3 categories was obtained.     

莲藕成分的近红外光谱分析模型的建立

目的:应用近红外光谱技术和化学计量学方法直接测定莲藕的常规指标。方法:用傅里叶变换近红外光谱仪采集样品的近红外漫反射光谱,再用传统理化分析方法测得样品的各品质参数;采用偏最小二乘(PLS)法建立定标模型,并采用内部交叉验证法对模型进行检验。结果:分别建立了莲藕水分、粗纤维、质构和糖度的PLS模型,其中质构的PLS模型最理想,模型的相关系数大于0.97;莲藕粗纤维、糖度和水分的PLS模型的相关系数均大于0.88。结论:采用近红外光谱法可以实现莲藕品质指标的快速无损检测。     

短波近红外光谱-偏最小二乘法在白酒分析中的应用

应用短波近红外光谱结合偏最小二乘法(NIRS-PLS)建立白酒中乙醇含量定量分析数学模型,通过选取最佳波长范围和最适主因子数对模型进行优化。应用所建模型对预测集和实际白酒样品中乙醇含量进行预测,取得了令人满意的结果。该方法方便快捷、无污染、可在线检测,且重现性、稳定性均良好,可作为白酒原位质量检测和在线质量监控的方法予以推广。     

Application of near infrared spectroscopy to peanut grading and quality analysis: overview

Techniques using near infrared (NIR) spectroscopy for quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. NIR spectroscopy has several advantages over conventional physical and chemical analytical methods of food quality analysis. It is a rapid and non destructive method and provides more information about the components and its structure present in the food products. It can measure more than one parameter simultaneously. The NIR spectrum includes wavelengths from 750 to 3000 nm that follow immediately after the visible region (400–700 nm). Many organic compounds can be well-defined by NIR reflectance, transmittance or diffuse reflectance system. This paper reviews the application of NIR spectroscopy to several oil seeds and examines the feasibility of using this technique for peanut quality analysis. The NIR spectroscopic instrumentation has been explained briefly for a better understanding. Also needs and limitations in use of NIR spectroscopy for peanut quality analysis and grading were explained.     

基于偏最小二乘法的板栗近红外光谱分析模型的建立

目的:应用近红外光谱技术和化学计量学方法,建立板栗品质分析的近红外光谱模型。方法:采用傅里叶变换近红外光谱仪,采集样品的近红外漫反射光谱,再用传统理化分析方法测得样品的各项品质参数,采用偏最小二乘法(PLS)建立定标模型,内部交叉验证法对模型进行检验。结果:对板栗分别建立了水分、淀粉、硬度和糖度的PLS模型,4种PLS模型都非常理想,模型的相关系数均大于0.99。结论:采用近红外光谱法可以实现板栗品质指标的快速无损检测。     

近红外光谱法快速测定香菇总糖含量

为弥补国标检测方法测定香菇总糖含量耗时长、步骤繁琐的缺陷,创建近红外(near infrared,NIR)光谱技术在测定香菇总糖含量方面应用,采用NIR分析技术与偏最小二乘算法(partial least square,PLS)建立香菇总糖的NIR分析模型,并对模型进行参数优化.实验共收集了106批样品,从中随机抽取1...     

近红外漫反射光谱法测试醋酸纤维滤棒中的三醋酸甘油酯

为探索快速准确测定滤棒中三醋酸甘油酯含量的方法,利用近红外漫反射光谱技术和偏最小二乘法,建立了滤棒中三醋酸甘油酯含量测定的数学模型,并对所建立的数学模型进行了优化和验证。结果表明,该方法具有简单快速、无损、准确的特点,无需样品预处理,特别适合于大量重复性样品的分析测定。     

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.     

Simultaneous determination of pyruvate and acetate levels in xanthan biopolymer by infrared spectroscopy: effect of spectral pre-processing for solid-state analysis

A rapid, direct, and reagent-free procedure based on solid-state Fourier transform infrared spectroscopy (FT-IR) coupled with partial least squares (PLS) data analysis has been developed for simultaneous determination of pyruvate and acetate levels in a microbial xanthan biopolymer. The influences of various spectral pre-processing procedures were studied in order to eliminate effects caused by sample preparation. It was determined that the combination of first derivative and orthogonal signal correction pre-processing contributes to a significant increase in the predictive performance of PLS-1 regression models. By employing the wavenumber region 1320–1350 cm⁻¹ for pyruvate determination and 1500–1600 cm⁻¹ for acetate determination, the root mean square error of cross-validation (RMSECV) for pyruvate and acetate contents were obtained 0.13% and 0.29% w/w, respectively. Results of the proposed procedure for different real samples and those obtained by their reference methods were compared.     

Use of Fourier Transform Infrared Spectroscopy in Combination with Partial Least Square for Authentication of Black Seed Oil

Fourier transform infrared spectroscopy in combination with multivariate calibration of partial least square is intended for quantitative analysis of black seed oil in binary mixture with sunflower oil and walnut oil, as well as in ternary mixture with sunflower oil and walnut oil. The spectra of black seed oil, sunflower oil, walnut oil, and their mixture with certain concentration were scanned using attenuated total reflectance at mid infrared region of 4000–650 cm^?1. For quantitatve analysis, Fourier transform infrared spectral treatment (normal or derivatives) with the highest values of coefficient of determination (R²) and the lowest values of root mean square error of calibration was selected as optimal calibration model. Partial least square at whole mid infrared region of 4000–650 cm^?1 is well suited for quantitative analysis of black seed oil either in binary mixture or ternary mixture with walnut oil and sunflower oil. Furthermore, using absorbancies at frequency region of 3009–721 cm^?1, principal component analysis is succesfully used for classification of black seed oil and that mixed with sunflower oil and walnut oil. The developed method is rapid, no sample preparation needed, and is not involving the use of chemical reagents and solvents.