The use of near infrared reflectance spectroscopy to identify the origin of oak shavings used in wine aging

Near infrared (NIR) reflectance spectroscopy combined with chemometrics was used to classify toasted and untoasted oak wood shavings sourced from two countries (France and USA). Oak wood shaving samples (n = 96) were scanned in the NIR region (680–2,500 nm) using a monochromator instrument operating in reflectance mode. Principal component analysis, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples according to their country of origin and level of toasting. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. Correct classification rates of 83, 87 and 100 % for samples sourced from USA, France and toasted treatment were obtained using PLS-DA. For LDA, correct classification rates of 80.4, 85 and 100 % were achieved for samples sourced from USA, France and toasted treatment, respectively. These results demonstrated the ability of NIR spectroscopy to discriminate between oak wood shavings sourced from two different countries and two levels of toasting.     

Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

The combination of UV, visible (Vis), near-infrared (NIR) and mid-infrared (MIR) spectroscopy with multivariate data analysis was explored as a tool to classify commercial Sauvignon Blanc (Vitis vinifera L., var. Sauvignon Blanc) wines from Australia and New Zealand. Wines (n = 64) were analysed in transmission using UV, Vis, NIR and MIR regions of the electromagnetic spectrum. Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) were used to classify Sauvignon Blanc wines according to their geographical origin using full cross validation (leave-one-out) as a validation method. Overall PLS-DA models correctly classified 86% of the wines from New Zealand and 73%, 86% and 93% of the Australian wines using NIR, MIR and the concatenation of NIR and MIR, respectively. Misclassified Australian wines were those sourced from the Adelaide Hills of South Australia. These results demonstrate the potential of combining spectroscopy with chemometrics data analysis techniques as a rapid method to classify Sauvignon Blanc wines according to their geographical origin.     

Combining Partial Least Squares (PLS) Discriminant Analysis and Rapid Visco Analyser (RVA) to Classify Barley Samples According to Year of Harvest and Locality

The aim of this study was to evaluate the usefulness of the Rapid Visco Analyser (RVA) instrument combined with pattern recognition methods as tools to differentiate commercial barley samples from two South Australian localities and three harvests. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise discriminant analysis were applied to classify samples based on the RVA profiles using full cross validation (leave-one-out) as the validation method. The PLS-DA models correctly classify 96.3 and 97.8 % of the barley samples according to harvest and locality, using the profiles generated by the RVA instrument. Analysis and interpretation of the eigenvectors and loadings from the PCA or PLS-DA models developed verified that the RVA profiles contain relevant information related to starch pasting properties that allows sample classification. These results suggest that RVA coupled with PLS-DA holds necessary information for a successful classification of barley samples sourced from different localities and harvests.     

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

The aim of this study was to evaluate the usefulness of visible (VIS), near-infrared reflectance (NIR) and mid-infrared (MIR) spectroscopy combined with pattern recognition methods as tools to differentiate grape juice samples from commercial Australian Chardonnay (n = 121) and Riesling (n = 91) varieties. Principal component analysis (PCA), partial least squares discriminant analysis and linear discriminant analysis (LDA) were applied to classified grape juice samples according to variety based on both NIR and MIR spectra using full cross-validation (leave-one-out) as a validation method. Overall, LDA models correctly classify 86% and 80% of the grape juice samples according to variety using MIR and VIS-NIR, respectively. The results from this study demonstrated that spectral differences exist between the juice samples from different varietal origins and confirmed that the infrared (IR) spectrum contains information able to discriminate among samples. Furthermore, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juice has enough information to allow the prediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary information for a successful classification of juice samples of different varieties.     

Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries

Visible (VIS) and near infrared (NIR) spectroscopy combined with chemometrics was used in an attempt to classify commercial Riesling wines from different countries (Australia, New Zealand, France and Germany). Commercial Riesling wines (n = 50) were scanned in the VIS and NIR regions (400–2500 nm) in a monochromator instrument, in transmission mode. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA) based on PCA scores were used to classify Riesling wines according to their country of origin. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. PLS-DA models correctly classified 97.5%, 80% and 70.5% of the Australian, New Zealand and European (France and Germany) Riesling wines, respectively. SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively. These results demonstrated that the VIS and NIR spectra contain information that when used with chemometrics allow discrimination between wines from different countries. To further validate the ability of VIS–NIR to classify white wine samples, a larger sample set will be required.     

Discrimination of yerba mate (<Emphasis Type="Italic">Ilex paraguayensis</Emphasis> St. Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis

Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100–2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross validation was used as validation method when classification models were developed. The overall classification rates obtained were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be used more productively on suspect specimens.     

Unfrazzled by Fizziness: Identification of Beers Using Attenuated Total Reflectance Mid-infrared Spectroscopy and Multivariate Analysis

Mid-infrared (MIR) spectroscopy coupled with attenuated total reflectance (ATR) was used to analyse a series of different beer types in order to confirm their identity (e.g. ale vs lager, commercial vs craft beer). Multivariate data analyses such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyse and to discriminate the beer samples analysed based on their infrared spectra. Correct classification rates of 100% were achieved in order to differentiate between ale and lager and also between commercial and craft beer sample types, respectively. Overall, the results of this study demonstrated the capability of MIR spectroscopy combined with PLS-DA to classify beer samples according to style (ale vs lager) and production (commercial vs craft). Furthermore, dissolved gases in the beer products were proven not to interfere as overlapping artefacts in the analysis. The benefits of using MIR-ATR for rapid and detailed analysis coupled with multivariate analysis can be considered a valuable tool for researchers and brewers interested in quality control, traceability and food adulteration. The novelty of this study is potentially far reaching, whereby customs and agencies can utilise these methods to mitigate beverage fraud.     

FTIR Spectroscopy and PLS-DA Classification and Prediction of Four Commercial Grade Virgin Olive Oils from Morocco

Fourier transform infrared (FTIR) coupled to chemometrics was shown to be a useful method to classify and predict the quality of four commercial grade virgin olive oils (VOO). FTIR and physicochemical data were collected using a set of 70 samples representing extra virgin (EV), virgin (V), ordinary virgin (OV), and lampante (L) commercial grade olive oils collected in Beni Mellal region (central Morocco). Two partial least squares discriminant analysis (PLS-DA) models using physicochemical data and FTIR data were established and compared. The PLS-DA model using only physicochemical data was not accurate enough to distinguish satisfactorily among OV, V, and EV olive oil grades. On the contrary, the PLS-DA model on FTIR data was better in the calibration, able to describe 98 % of the spectral information and predicting 93 % of the VOO grades. In the external validation, this PLS-DA model accurately classified VOO commercial grades with prediction accuracy of 100 %. The proposed procedure is fast, nondestructive, simple, and easy to operate, and it is recommended for the quick monitoring of olive oil’s quality.     

Simultaneous Classification of Teas According to Their Varieties and Geographical Origins by Using NIR Spectroscopy and SPA-LDA

Due to the human health benefits already scientifically proven, tea (Camellia sinensis) has been widely studied in the literature. Several studies report the classification of the variety or geographical origin of teas, separately. Thus, this paper has proposed a methodology for simultaneous classification of tea samples according to their varieties (green or black) and geographical origins (Brazil, Argentina, or Sri Lanka). For this purpose, near-infrared (NIR) spectroscopy and three differing supervised pattern recognition techniques, namely SIMCA (soft independent modeling of class analogy), PLS-DA (partial least squares-discriminant analysis), and SPA-LDA (successive projections algorithm associated with linear discriminant analysis) have been used. Despite having good results, both full-spectrum PLS-DA and SIMCA were not able to achieve 100 % classification accuracy, regardless of the significance level for the F test in the case of the SIMCA model. On the other hand, the resulting SPA-LDA model successfully classified all studied samples into five differing tea classes (Argentinean green tea; Brazilian green tea; Argentinean black tea; Brazilian black tea; and Sri Lankan black tea) using 12 wave numbers alone.     

NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley

ABSTRACT

Deoxynivalenol (DON) is one of the mycotoxins produced mainly by the Fusarium graminearum species complex in small grain cereals, including barley. This toxin can cause alimentary disorders, immune function depression and gastroenteritis. The negative health effects associated with DON coupled to the increasing concern about green and rapid methods of analysis motivated this study. In this context, near infrared (NIR) spectroscopy data were applied for exploratory analysis to distinguish barley with high and low levels of DON contamination (> or <1250 µg/kg according to the European Union threshold), by Partial Least Squares-Discriminant Analysis (PLS-DA), and to verify the performance of Partial Least Squares-Regression (PLS-R) to predict DON concentration in barley samples. Maximum values of specificity and sensitivity were achieved in the calibration set; 90.9% and 81.9% were observed in the cross-validation set for the PLS-DA classification model. PLS-R quantification of DON in barley presented low values of error (RMSEC = 101.94 µg/kg and RMSEP = 160.76 µg/kg). Thus, we found that NIR in combination with adequate chemometric tools could be applied as a green technique to monitor DON contamination in barley.     

An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals

Although both near infrared (NIR) spectroscopy and mid infrared (MIR) spectroscopy combined with multivariate data analysis (MVA) have been extensively used to measure chemical composition (e.g. protein, moisture, oil) in a wide number of grains few reports can be found on the use of this methods for varietal discrimination and traceability of cereals. In this overview applications of NIR spectroscopy and MIR spectroscopy combined with multivariate data methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA) to aid on the authentication and traceability of cereals are discussed.     

Mid infrared spectroscopy and multivariate analysis: A tool to discriminate between organic and non-organic wines grown in Australia

The combination of mid infrared (MIR) spectroscopy and multivariate analysis was explored as a tool to classify commercial wines sourced from organic (ORG) and non-organic (NORG) production systems. Commercial ORG (n = 57) and NORG (n = 115) red and white wine samples from 13 growing regions in Australia were analysed using a MIR spectrophotometer. Discriminant models based on MIR spectra were developed using principal component analysis (PCA), discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Overall, the LDA models based on the PCA scores correctly classified on average, more than 75% of the wine samples while the DPLS models correctly classified more than 85% of the wines belonging to ORG and NORG production systems, respectively. These results showed that MIR combined with discriminant techniques might be a suitable method that can be easily implemented by the wine industry to classify wines produced under organic systems.     

Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data

The potential of mid-infrared (MIR) and near-infrared (NIR) spectroscopy for their ability to differentiate between apple juice samples on the basis of apple variety and applied heat-treatment was evaluated. The heat-treatment involved exposure of juice samples (15 ml) for 30 s in a 900 W microwave oven and the apple varieties used to produce the juice samples were Bramley, Elstar, Golden Delicious and Jonagold. The chemometric procedures applied to the MIR and NIR data were partial least squares regression (PLS1 for differentiation on the basis of heat-treatment, PLS2 for varietal differentiation) and linear discriminant analysis (LDA) applied to principal component (PC) scores. PLS1 and PLS2 gave the highest level of correct classification of the apple juice samples according to heat-treatment (77.2% for both MIR and NIR data) and variety (78.3–100% for MIR data; 82.4–100% for NIR data), respectively.     

基于GC-MS技术对不同香型白酒的判别分析

基于气相色谱-质谱（GC-MS）联用技术检测5种香型白酒挥发性风味成分,并结合聚类分析（CA）、主成分分析（PCA）及偏最小二乘判别分析（PLS-DA）等化学计量学手段分析检测结果,并对不同白酒的香型进行识别和分类。结果表明,不同香型白酒样品中的风味成分具有明显差异,PLS-DA中的2个成分能很好的代表酒样中的基本信息,5种香型的组心质都相互分离,没有重合,将“测试集”和“训练集”的判定结果与实际结果相比较,正确率均为100%。因此,利用GC-MS技术结合化学计量学方法,可用于不同香型白酒的鉴别分析。     

基于多源光谱分析技术的鱼油品牌判别方法研究

多源光谱分析技术被用于鱼油品牌快速无损鉴别。采用可见光谱分析技术、短波近红外光谱分析技术、长波近红外光谱分析技术、中红外光谱分析技术和核磁共振光谱分析技术采集了7种不同品牌的鱼油的光谱特征,并应用偏最小二乘判别分析法(partial least squares discrimination analysis,PLS-DA)和最小二乘支持向量机(least-squares support vector machine,LS-SVM)建立判别模型并比较判别结果。基于长波近红外光谱的PLS-DA模型和LS-SVM模型取得了最高识别正确率,建模集和预测集识别正确率均达到100%。采用中红外光谱和核磁共振谱分别建立的LS-SVM模型,也可以获得100%的判别正确率。而可见光谱和短波近红外光谱则判别准确率较差。且LS-SVM算法较PLS-DA更加适合用于建立光谱数据和鱼油品牌之间的判别模型。研究结果表面长波近红外光谱技术能够有效判别不同鱼油的品牌,为将来鱼油品质鉴定便携式仪器的开发提供了技术支持和理论依据。     

Feasibility of Discriminating Dried Dairy Ingredients and Preheat Treatments Using Mid-Infrared and Raman Spectroscopy

This study investigated the feasibility of mid-infrared (MIR) and Raman spectroscopy for (i) discrimination of three dried dairy ingredients, namely skim milk powder (SMP), whey protein concentrate (WPC) and demineralised whey protein (DWP) powder, and (ii) discrimination of preheat treatments of dried dairy ingredients using partial least squares discriminant analysis (PLS-DA). PLS1-DA models developed using MIR ranges of 800–1800 and 1200–1800 cm^?1 yielded the best discrimination (correct identification of 97.2% for SMP discrimination and 100% for WPC and DWP discrimination). The best PLS2-DA model using MIR spectroscopy was developed over the spectral range of 800–1800 cm^?1 and produced correct identification of 100% for dairy ingredient discrimination. Models developed using Raman 800–1800 and 1200–1800 cm^?1 spectral ranges correctly discriminated (100% correctly identified) each dairy ingredient. Although all PLS1-DA and PLS2-DA models developed using both spectral technologies for preheat treatment discrimination had good discrimination accuracy (86–100%), they employed a high number of factors (8–9 for the best model). The use of the Martens uncertainty test successfully reduced the number of factors employed (3–4 for the best models) and improved the performance of PLS1-DA models for preheat treatment discrimination (all 100% correctly identified). This feasibility study demonstrates the potential of both MIR and Raman spectroscopy for rapid characterisation of dried dairy ingredients.     

Biogenic Amine Content in Red Wines from Different Protected Designations of Origin of Southern Italy: Chemometric Characterization and Classification

Biogenic amines are contaminants naturally present in wines. Their occurrence is influenced by several factors including oenological and agricultural practices, grape variety, and geographical origin. For these reasons, they have been chosen as marker to characterize and classify 56 Italian red wines belonging to four protected designations of origin (PDO) from Southern Italy. Principal component analysis and cluster analysis were applied on data obtained by HPLC/RF in order to highlight the natural grouping of samples. Afterward, linear discriminant analysis and partial least squares were used to classify the wines according to their PDO. Biogenic amines are demonstrated to be a reliable and useful marker for the characterization and classification of the four Southern Italian PDOs investigated. Both the linear discriminant analysis (LDA) and the partial least squares discriminant analysis (PLS-DA) achieved 100 % of wines correctly classified and predicted. Therefore, the determination of these compounds in red wines can play an important role in wine quality assessment, by providing information for the prevention of potential detrimental effects on health and for the characterization of PDO labeled wines.     

Simultaneous detection for adulterations of maltodextrin,sodium carbonate,and whey in raw milk using Raman spectroscopy and chemometrics

To achieve rapid on-site identification of raw milk adulteration and simultaneously quantify the levels of various adulterants, we combined Raman spectroscopy with chemometrics to detect 3 of the most common adulterants. Raw milk was artificially adulterated with maltodextrin (0.5–15.0%; wt/wt), sodium carbonate (10–100 mg/kg), or whey (1.0–20.0%; wt/wt). Partial least square discriminant analysis (PLS-DA) classification and a partial least square (PLS) regression model were established using Raman spectra of 144 samples, among which 108 samples were used for training and 36 were used for validation. A model with excellent performance was obtained by spectral preprocessing with first derivative, and variable selection optimization with variable importance in the projection. The classification accuracy of the PLS-DA model was 95.83% for maltodextrin, 100% for sodium carbonate, 95.84% for whey, and 92.25% for pure raw milk. The PLS model had a detection limit of 1.46% for maltodextrin, 4.38 mg/kg for sodium carbonate, and 2.64% for whey. These results suggested that Raman spectroscopy combined with PLS-DA and PLS model can rapidly and efficiently detect adulterants of maltodextrin, sodium carbonate, and whey in raw milk.     

Classification of moroccan olive cultivars by linear discriminant analysis applied to ATR–FTIR spectra of endocarps

The potential of FTIR combined with chemometrics was studied to classify five Moroccan varieties of olives by analysis on the endocarps. Attenuated total reflectance (ATR) enabled the samples to be examined directly in the solid state. The spectral data were subjected to a preliminary derivative elaboration based on the Norris gap algorithm to reduce the noise and extract larger analytical information. Linear discriminant analysis (LDA) was adopted as classification method, and Principle component analysis (PCA) was employed to compress the original data set into a reduced new set of variables before LDA. The calibration set was built by using the IR data from seventy‐five samples scanned in reflectance mode, and the ranges 3000–2400 and 2300–600 cm^?1 were selected because furnishing the most useful analytical information. PCA allowed clustering the samples in five classes by using the first two principal components with an explained variance of 98.16%. Application of LDA on an external test set of twenty‐five samples enabled to classify them into five variety groups with a correct classification of 92.0%.     

基于拉曼光谱的大米快速分类判别方法

以拉曼光谱技术为手段,结合化学计量学方法,对来自黑龙江、江苏、湖南3个产地共123份大米样品的光谱数据进行采集,并对得到的拉曼图谱进行主成分分析(PCA)和偏最小二乘判别分析(PLSDA),建立大米快速分类判别方法。应用主成分分析对不同种类、产地和品种的大米进行粗分类鉴别;选择不同种类、品种和产地的稻米样本建立相应的偏最小二乘判别分析模型,其中2/3的样本作为建模训练集,1/3的样本作为建模校正集,按照种类、产地、品种建立的模型其训练集样本正确判别率均为100%,校正集样本正确判别率分别为100%,100%,94.12%。因此,研究所建立的拉曼光谱技术结合化学计量学方法可以快速、有效地鉴别大米种类、品种及产地。