Detection and identification of bacteria in an isolated system with near-infrared spectroscopy and multivariate analysis

Near-infrared (NIR) transflectance spectra of Listeria innocua FH, Lactococcus lactis, Pseudomonas fluorescens, Pseudomonas mendocina, and Pseudomonas putida suspensions were collected and investigated for their potential use in the identification and classification of bacteria. Unmodified spectral data were transformed (first and second derivative) using the Savitzsky-Golay algorithm. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA), and soft independent modeling of class analogy (SIMCA) were used in the analysis. Using either full cross-validation or separate calibration and prediction data sets, PLS2 regression classified the five bacterial suspensions with 100% accuracy at species level. At Pseudomonas genus level, PLS2 regression classified the three Pseudomonas species with 100% accuracy. In the case of SIMCA, prediction of an unknown sample set produced correct classification rates of 100% except for L. innocua FH (77%). At genus level, SIMCA produced correct classification rates of 96.7, 100, and 100% for P. fluorescens, P. mendocina, and P. putida, respectively. This successful investigation suggests that NIR spectroscopy can become a useful, rapid, and noninvasive tool for bacterial identification.     

Application of Fourier transform midinfrared spectroscopy to the discrimination between Irish artisanal honey and such honey adulterated with various sugar syrups

A collection of authentic artisanal Irish honeys (n = 580) and certain of these honeys adulterated by fully inverted beet syrup (n = 280), high-fructose corn syrup (n = 160), partial invert cane syrup (n = 120), dextrose syrup (n = 160), and beet sucrose (n = 120) was assembled. All samples were adjusted to 70 degrees Bx and scanned in the midinfrared region (800-4000 cm(-1)) by attenuated total reflectance sample accessory. By use of soft independent modeling of class analogy (SIMCA) and partial least-squares (PLS) classification, authentic honey and honey adulterated by beet sucrose, dextrose syrups, and partial invert corn syrup could be identified with correct classification rates of 96.2%, 97.5%, 95.8%, and 91.7%, respectively. This combination of spectroscopic technique and chemometric methods was not able to unambiguously detect adulteration by high-fructose corn syrup or fully inverted beet syrup.     

Authentication of the botanical origin of honey by front-face fluorescence spectroscopy. A preliminary study

The potential of front-face fluorescence spectroscopy for the authentication of unifloral and polyfloral honey types (n = 57 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis was evaluated. Emission spectra were recorded between 280 and 480 nm (excit: 250 nm), 305 and 500 nm (excit: 290 nm), and 380 and 600 nm (excit: 373 nm) directly on honey samples. In addition, excitation spectra (290-440 nm) were recorded with the emission measured at 450 nm. A total of four different spectral data sets were considered for data analysis. After normalization of the spectra, chemometric evaluation of the spectral data was carried out using principal component analysis (PCA) and linear discriminant analysis (LDA). The rate of correct classification ranged from 36% to 100% by using single spectral data sets (250, 290, 373, 450 nm) and from 73% to 100% by combining these four data sets. For alpine polyfloral honey and the unifloral varieties investigated (acacia, alpine rose, honeydew, chestnut, and rape), correct classification ranged from 96% to 100%. This preliminary study indicates that front-face fluorescence spectroscopy is a promising technique for the authentication of the botanical origin of honey. It is nondestructive, rapid, easy to use, and inexpensive. The use of additional excitation wavelengths between 320 and 440 nm could increase the correct classification of the less characteristic fluorescent varieties.     

NIR‐FT/Raman Spectroscopy for Nutritional Classification of Cereal Foods

The classification of cereals using near‐infrared Fourier transform Raman (NIR‐FT/Raman) spectroscopy was accomplished. Cereal‐based food samples (n = 120) were utilized in the study. Ground samples were scanned in low‐iron NMR tubes with a 1064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a Ge (LN₂) detector over the Raman shift range of 202.45~3399.89 cm^‐1. Samples were classified based on their primary nutritional components (total dietary fiber [TDF], fat, protein, and sugar) using principle component analysis (PCA) to extract the main information. Samples were classified according to high and low content of each component using the spectral variables. Both soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) regression based classification were investigated to determine which technique was the most appropriate. PCA results suggested that the classification of a target component is subject to interference by other components in cereal. The Raman shifts that were most responsible for classification of each component were 1600~1630 cm^‐1 for TDF, 1440 and 2853 cm^‐1 for fat, 2910 and 1660 cm^‐1 for protein, and 401 and 848 cm^‐1 for sugar. The use of the selected spectral region (frequency region) for each component produced better results than the use of the entire region in both SIMCA and PLS‐based classifications. PLS‐based classification performed better than SIMCA for all four components, resulting in correct classification of samples 85~95% of the time. NIR‐FT/Raman spectroscopy represents a rapid and reliable method by which to classify cereal foods based on their nutritional components.     

Supervised pattern recognition procedures for discrimination of whiskeys from gas chromatography/mass spectrometry congener analysis

The volatile congener analysis of 52 commercialized whiskeys (24 samples of single malt Scotch whiskey, 18 samples of bourbon whiskey, and 10 samples of Irish whiskey) was carried out by gas chromatography/mass spectrometry after liquid-liquid extraction with dichloromethane. Pattern recognition procedures were applied for discrimination of different whiskey categories. Multivariate data analysis includes linear discriminant analysis (LDA), k nearest neighbors (KNN), soft independent modeling of class analogy (SIMCA), procrustes discriminant analysis (PDA), and artificial neural networks techniques involving multilayer perceptrons (MLP) and probabilistic neural networks (PNN). Classification rules were validated by considering the number of false positives (FPs) and false negatives (FNs) of each class associated to the prediction set. Artificial neural networks led to the best results because of their intrinsic nonlinear features. Both techniques, MLP and PNN, gave zero FPs and zero FNs for all of the categories. KNN is a nonparametric method that also provides zero FPs and FNs for every class but only when selecting K = 3 neighbors. PDA produced good results also (zero FPs and FNs always) but only by selecting nine principal components for class modeling. LDA shows a lesser classification performance, because of the building of linear frontiers between classes that does not apply in many real situations. LDA led to one FP for bourbons and one FN for scotches. The worse results were obtained with SIMCA, which gave a higher number of FPs (five for both scotches and bourbons) and FNs (six for scotchs and two for bourbons). The possible cause of these findings is the strong influence of class inhomogeneities on the SIMCA performance. It is remarkable that in any case, all of the methodologies lead to zero FPs and FNs for the Irish whiskeys.     

Discrimination between Shiraz wines from different Australian regions: the role of spectroscopy and chemometrics

This study reports the use of UV-visible (UV-vis), near-infrared (NIR), and midinfrared (MIR) spectroscopy combined with chemometrics to discriminate among Shiraz wines produced in five Australian regions. In total, 98 commercial Shiraz samples (vintage 2006) were analyzed using UV-vis, NIR, and MIR wavelength regions. Spectral data were interpreted using principal component analysis (PCA), linear discriminant analysis (LDA), and soft independent model of class analogy (SIMCA) to classify the wine samples according to region. The results indicated that wine samples from Western Australia and Coonawarra can be separated from the other wines based on their MIR spectra. Classification results based on MIR spectra also indicated that LDA achieved 73% overall correct classification, while SIMCA 95.3%. This study demonstrated that IR spectroscopy combined with chemometric methods can be a useful tool for wine region discrimination.     

Preliminary studies for the differentiation of apple juice samples by chemometric analysis of solid-phase microextraction-gas chromatographic data

A combination of gas chromatography (GC) and chemometrics was evaluated for its ability to differentiate between apple juice samples on the basis of apple variety and applied heat treatment. The heat treatment involved exposure of 15 mL juice samples for 30 s in a 900 W domestic microwave oven. The chromatographic results were subjected to two chemometric procedures: (1) partial least squares (PLS) regression and (2) linear discriminant analysis (LDA) applied to principal component (PC) scores. The percent correct classification of samples were obtained from PLS and LDA in terms of separation on the basis of apple variety and applied heat treatment. PLS gave the highest level of correct classification of the apple juice samples according to both variety and heat treatment, 92.5% correct classification in each case. When LDA was performed on the PC scores obtained from GC analysis, 87.5% and 80% of samples were correctly classified according to apple variety used and applied heat treatment, respectively.     

Initial study of honey adulteration by sugar solutions using midinfrared (MIR) spectroscopy and chemometrics

Fourier transform infrared (FTIR) spectroscopy and attenuated total reflection (ATR) sampling have been used to detect adulteration of honey samples. The sample set comprised 320 spectra of authentic (n = 99) and adulterated (n = 221) honeys. Adulterants used were solutions containing both d-fructose and d-glucose prepared in the following respective weight ratios: 0.7:1.0, 1.2:1.0 (typical of honey composition), and 2.3:1.0. Each adulterant solution was added to individual honeys at levels of 7, 14, and 21% w/w. Spectral data were compressed and analyzed using k-nearest neighbors (kNN) and partial least squares (PLS) regression techniques. A number of data pretreatments were explored. Best classification models were achieved with PLS regression on first derivative spectra giving an overall correct classification rate of 93%, with 99% of samples adulterated at levels of 14% w/w or greater correctly identified. This method shows promise as a rapid screening technique for detection of this type of honey adulteration.     

基于近红外光谱技术的蜂蜜掺假识别

为了实现蜂蜜掺假的快速识别,应用近红外光谱结合模式识别方法对蜂蜜掺假现象进行了识别分析。该研究收集了中国不同品种、不同地域的典型天然蜂蜜样品,根据目前市场上常见的蜂蜜掺假手段,掺假物质及相对含量情况配制了掺假蜂蜜样品,利用傅立叶近红外光谱仪采集其透反射近红外光谱,分别采用偏最小二乘判别分析（PLS-DA）,独立软模式法（SIMCA）,误差反向传播神经网络（BP-ANN）和最小二乘支持向量机（LS-SVM）等模式识别方法,进行蜂蜜掺假识别研究。研究结果表明：利用这4种方法在蜂蜜中掺入果葡糖浆和果葡糖水的情况下均能很好地识别出掺假蜂蜜样品,其中对于掺入果葡糖浆的掺假情况,校正集的正确判别率均达到95%以上,验证集的正确判别率均达到87%以上,对于掺入果葡糖水的掺假蜂蜜校正集的正确判别率均达到93%以上,验证集的正确判别率均达到84%以上。通过比较4种不同的识别算法,发现采用LS-SVM时,对两种掺假情况下校正集和验证集的正确判别率均达到了100%,表明基于近红外光谱的蜂蜜掺假快速准确识别是可行的。     

Geographical characterization of polyfloral and acacia honeys by nuclear magnetic resonance and chemometrics

The importance of geographical origin determination is an increasing and pressing requirement for all foods. Honey is one of the largest studied foods due to its nutritional and medicinal properties in a correct diet. In this paper, a total of 41 honey samples (polyfloral and acacia) from different countries have been analyzed in terms of (1)H NMR spectroscopy coupled with multivariate statistical methods. Unsupervised principal component analysis resulted as an efficient tool in distinguishing (1)H NMR spectra of polyfloral and acacia honey samples and for geographical characterization of the latter ones. Hierarchical projection to latent structures discriminant analysis was successfully applied for the discrimination among polyfloral honey samples of different geographical origins. (13)C NMR spectroscopy was applied to honey samples with the aim to investigate possible sugar isoforms differentiation. Our preliminary data indicated a different isoforms ratio between betaFP and betaFF only for polyfloral Argentinean samples, while Hungarian samples showed resonance shifts for some carbons of alphaFF, betaFP, betaFF, and alphaGP isoforms for both varieties. These data confirmed the potentiality of (13)C spectroscopy in food characterization, especially in sugar-based foods.     

Application of cryoprobe 1H nuclear magnetic resonance spectroscopy and multivariate analysis for the verification of corsican honey

Proton nuclear magnetic resonance spectroscopy ((1)H NMR) and multivariate analysis techniques have been used to classify honey into two groups by geographical origin. Honey from Corsica (Miel de Corse) was used as an example of a protected designation of origin product. Mathematical models were constructed to determine the feasibility of distinguishing between honey from Corsica and that from other geographical locations in Europe, using (1)H NMR spectroscopy. Honey from 10 different regions within five countries was analyzed. (1)H NMR spectra were used as input variables for projection to latent structures (PLS) followed by linear discriminant analysis (LDA) and genetic programming (GP). Models were generated using three methods, PLS-LDA, two-stage GP, and a combination of PLS and GP (PLS-GP). The PLS-GP model used variables selected by PLS for subsequent GP calculations. All models were generated using Venetian blind cross-validation. Overall classification rates for the discrimination of Corsican and non-Corsican honey of 75.8, 94.5, and 96.2% were determined using PLS-LDA, two-stage GP, and PLS-GP, respectively. The variables utilized by PLS-GP were related to their (1)H NMR chemical shifts, and this led to the identification of trigonelline in honey for the first time.     

近红外光谱结合小波变换预处理在蜂蜜产地快速检测中的应用

应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析。kennard-Stone法划分训练集和预测集。光谱用一阶导数加自归一化预处理后,再用小波变换（WT）进行压缩和滤噪。结合滤波后光谱信息,分别用径向基神经网络（RBFNN）和偏最小二乘-线性判别分析（PLS-LDA）建立了苹果蜜产地和油菜蜜产地判别模型。对不同小波基和分解尺度进行了详细讨论。对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度最好,都为96.2%。对油菜蜜：WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度最好;WT-PLS-LDA模型在小波基为db9、分解尺度也为1时,预测精度最好,为90.5%;预测精度WT-PLS-LDA模型优于WT-RBFNN模型。研究表明：WT结合线性的PLS-LDA建模比WT结合非线性的RBFNN建模更适于蜂蜜产地鉴别;近红外光谱结合WT-PLS-LDA可实现对蜂蜜产地的快速无损检测,为蜂蜜产地鉴别提供了一种新方法。     

Authentication of the botanical and geographical origin of honey by mid-infrared spectroscopy

The potential of Fourier transform mid-infrared spectroscopy (FT-MIR) using an attenuated total reflectance (ATR) cell was evaluated for the authentication of 11 unifloral (acacia, alpine rose, chestnut, dandelion, heather, lime, rape, fir honeydew, metcalfa honeydew, oak honeydew) and polyfloral honey types (n = 411 samples) previously classified with traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis, the error rates of the discriminant models being calculated by using Bayes' theorem. The error rates ranged from <0.1% (polyfloral and heather honeys as well as honeydew honeys from metcalfa, oak, and fir) to 8.3% (alpine rose honey) in both jackknife classification and validation, depending on the honey type considered. This study indicates that ATR-MIR spectroscopy is a valuable tool for the authentication of the botanical origin and quality control and may also be useful for the determination of the geographical origin of honey.     

Floral classification of honey using mid-infrared spectroscopy and surface acoustic wave based z-Nose Sensor

Fourier transform infrared spectroscopy (FTIR) and z-Nose were used as screening tools for the identification and classification of honey from different floral sources. Honey samples were scanned using microattenuated total reflectance spectroscopy in the region of 600-4000 cm(-1). Spectral data were analyzed by principal component analysis, canonical variate analysis, and artificial neural network for classification of the different honey samples from a range of floral sources. Classification accuracy near 100% was achieved for clover (South Dakota), buckwheat (Missouri), basswood (New York), wildflower (Pennsylvania), orange blossom (California), carrot (Louisiana), and alfalfa (California) honey. The same honey samples were also analyzed using a surface acoustic wave based z-Nose technology via a chromatogram and a spectral approach, corrected for time shift and baseline shifts. On the basis of the volatile components of honey, the seven different floral honeys previously mentioned were successfully discriminated using the z-Nose approach. Classification models for FTIR and z-Nose were successfully validated (near 100% correct classification) using 20 samples of unknown honey from various floral sources. The developed FTIR and z-Nose methods were able to detect the floral origin of the seven different honey samples within 2-3 min based on the developed calibrations.     

Authenticity of PGI "Clementine of Calabria" by multielement fingerprint

Clementine is a citrus fruit that has found a peculiar habitat in specific areas of Calabria, a region located in southern Italy. Due to its peculiar characteristics it was recently awarded with protected geographical indications (PGI) from the European Union. In this work, stepwise linear discriminant analysis (S-LDA), soft independent modeling of class analogy (SIMCA), and partial least-squares discriminant analysis (PLS-DA) were used to build chemometric models able to protect PGI Clementine from others of different origin. Accordingly, the concentration of 24-26 elements was determined in peel and juice samples, respectively, obtained from Calabrian PGI clementine and from fruits cultivated in Algeria, Tunisia, and Spain. A cross-validation procedure has shown very satisfactory values of prediction ability for both S-LDA (96.6% for juice samples and 100% for peel samples) and SIMCA (100% for both peel and juice samples). PLS-DA models also yielded satisfactory results.     

近红外光谱结合化学计量学方法检测蜂蜜产地

为了实现蜂蜜产地的快速判别,应用近红外光谱结合化学计量学方法对蜂蜜产地进行了判别分析。kennard-Stone法划分训练集和预测集。光谱用一阶导数加自归一化预处理后,再用小波变换（WT）进行压缩和滤噪。结合滤波后光谱信息,分别用径向基神经网络（RBFNN）和偏最小二乘-线性判别分析（PLS-LDA）建立了苹果蜜产地和油菜蜜产地的判别模型。对不同小波基和分解尺度进行了讨论。对苹果蜜,WT-RBFNN模型和WT-PLS-LDA模型都是小波基为db1、分解尺度为2时的预测精度较好,都为96.2%。对油菜蜜：WT-RBFNN模型在小波基为db4和分解尺度为1时,预测精度较好,为85.7%;WT-PLS-LDA模型在小波基为db9、分解尺度也为1时,预测精度较好,为90.5%。研究表明：WT结合线性的PLS-LDA建模比WT结合非线性的RBFNN建模更适于蜂蜜产地判别;近红外光谱技术具有快速判别蜂蜜产地的潜力。     

Authentication of the botanical origin of honey by near-infrared spectroscopy

Fourier transform near-infrared spectroscopy (FT-NIR) was evaluated for the authentication of eight unifloral and polyfloral honey types (n = 364 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis. The corresponding error rates were calculated according to Bayes' theorem. NIR spectroscopy enabled a reliable discrimination of acacia, chestnut, and fir honeydew honey from the other unifloral and polyfloral honey types studied. The error rates ranged from <0.1 to 6.3% depending on the honey type. NIR proved also to be useful for the classification of blossom and honeydew honeys. The results demonstrate that near-infrared spectrometry is a valuable, rapid, and nondestructive tool for the authentication of the above-mentioned honeys, but not for all varieties studied.     

Protection of originality of Tokaji Aszú: amines and organic acids in botrytized wines by high-performance liquid chromatography

Amine and organic acid composition of Aszú wines from the Tokaj region of Hungary, nonbotrytized Hungarian wines from different regions, and foreign botrytized wines were analyzed by high-performance liquid chromatography. Hungarian and foreign wines (36 Hungarian and seven foreign botrytized wines) were compared in different ways by calculation of ratios of given amine compounds, analyses of variance, principal component, and discriminant analysis. In wines, putrescine and in some samples 3-methyl-butylamine and/or phenyl ethylamine were found in remarkable concentrations, while in botrytized wines four other amines were verified in high concentration. Good separation between Aszú and foreign botrytized wines was found by calculation of the amine component's ratio. The first two principal components of the principal component analysis accounted for 77 and 84% of the total variance in the data of amines and acids, respectively. The component scores of samples grouped according to Aszú, foreign botrytized, and nonbotrytized wines. Linear discriminant analysis was used for differentiation of Aszú, foreign botrytized, and normal wines. Using nine amines and two acids as variables, the correct classification was 97.6%. On the basis of results, an objective evaluation method can be elaborated for quality control in order to protect the authenticity and origin of wine specialties made from botrytized grapes.     

Study of the phenolic profile of cider apple cultivars at maturity by multivariate techniques.

Phenolic compounds in 46 Spanish cider apple varieties were determined by RP-HPLC with direct injection. Several pattern recognition procedures, including principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares (PLS-1), were applied to the data in an attempt to classify the samples into bitter and nonbitter categories. Reliable decision rules were obtained by both LDA and PLS-1. LDA achieved 91.3 and 85.7% correct classification respectively, for internal and external evaluation of the model.     

Identification of botanical biomarkers in Argentinean Diplotaxis honeys: flavonoids and glucosinolates

To select and establish floral biomarkers of the botanical origin of Diplotaxis tenuifolia honeys, the flavonoids and glucosinolates present in bee-deposited nectar collected from hive combs (unripe honey) and mature honey from the same hives fron which the unripe honey samples were collected were analyzed by LC-UV-PAD-ESI-MS(n). Glycosidic conjugates of the flavonols quercetin, kaempferol, and isorhamnetin were detected and characterized in unripe honey. D. tenuifolia mature honeys contained the aglycones kaempferol, quercetin, and isorhamnetin. The differences between the phenolic profiles of mature honey and freshly deposited honey could be due to hydrolytic enzymatic activities. Aliphatic and indole glucososinolates were analyzed in unripe and mature honeys, this being the first report of the detection and characterization of glucosinolates as honey constituents. Moreover, these honey samples contained different amounts of propolis-derived flavonoid aglycones (1765-3171 μg/100 g) and hydroxycinnamic acid derivatives (29-1514 μg/100 g). Propolis flavonoids were already present in the freshly deposited nectar, showing that the incorporation of these compounds to honey occurs at the early steps of honey production. The flavonoids quercetin, kaempferol, and isorhamnetin and the glucosinolates detected in the samples could be used as complementary biomarkers for the determination of the floral origin of Argentinean Diplotaxis honeys.