Classification of geographical origins and prediction of δ13C and δ15N values of lamb meat by near infrared reflectance spectroscopy

Near infrared spectroscopy (NIRS) combined with chemometric analysis was investigated for its potential to classify the geographical origin and predict δ(13)C and δ(15)N values of lamb meat samples (n=99) from three pastoral regions and two agricultural regions of China. Principal component analysis (PCA), discriminant partial least squares analysis (D-PLS), linear discriminant analysis (LDA) and partial least squares regression (PLSR) were used for data analysis. D-PLS and LDA correctly classified 100% of the both pastoral and agricultural region samples, and gave a total correct classification of 88.9% and 75% to the five individual region samples, respectively. The best PLSR calibration models for predicting δ(13)C and δ(15)N of lamb meat were obtained with the determination coefficient (R(2)) 0.76 and 0.87, respectively. These results show that NIRS combined with chemometrics can be used as a rapid and effective method to discriminate the geographical origin and estimate the δ(13)C and δ(15)N of lamb meat.     

Tracing the geographical origin of honeys based on volatile compounds profiles assessment using pattern recognition techniques

The goal of this study was to examine the possibility of verifying the geographical origin of honeys based on the profiles of volatile compounds. A head-space solid phase microextraction (SPME) combined with comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) was used to analyze the volatiles in honeys with various geographical and floral origins. Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of volatile compounds. Specifically, linear discriminant analysis (LDA), soft independent modeling of class analogies (SIMCA), discriminant partial least squares (DPLS) and support vector machines (SVM) with the recently proposed Pearson VII universal kernel (PUK) were used in our study to discriminate between Corsican and non-Corsican honeys. Although DPLS and LDA provided models with high sensitivities and specificities, the best performance was achieved by the SVM using PUK. The results of this study demonstrated that GC × GC–TOFMS combined with methods like LDA, DPLS and SVM can be successfully applied to detect mislabeling of Corsican honeys.     

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.     

Identification of the Geographical Origins of Pomelos Using Multielement Fingerprinting

Eighty pomelo samples and 80 soil samples were examined using a multielement component test to predict the geographical origins of pomelos produced in 4 regions (Sichuan, Chongqing, Fujian, and Guangxi Provinces) of China. The concentrations of 8 elements were determined by atomic absorption spectrometry. Ca, K, and Na were the most abundant elements. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to reduce the dimensionality of the multielement data from 8 to 2 while retaining the highest possible variance. Using PCA and LDA, 69.66% and 91.30%, respectively, of the pomelo origins were classified correctly using multielement variables, along with 67.06% and 83.40% for soil multielement analysis. Results indicated that the LDA method was more effective for geographical origin classification than PCA. The results of the multielement component test demonstrated its capability to screen pomelo origins rapidly.     

Traceability of olive oil based on volatiles pattern and multivariate analysis

An automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography–ion trap mass spectrometry (GC–ITMS), was developed and employed for fast characterisation of olive oil volatiles. In total, 914 samples were collected, over three production seasons, in north-western Italy—Liguria (n = 210) and other regions—in addition to the rest of Italy, Spain, France, Greece, Cyprus, and Turkey (n = 704) with the aim to distinguish, based on analytical (profiling) data, the olive oils labelled as “Ligurian” (protected denomination of origin region, PDO) from all the others (“non-Ligurian”). For the chemometric analysis, linear discriminant analysis (LDA) and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. Employing LDA, somewhat lower recognition (81.4%) and prediction (61.7%) abilities were obtained. The classification model was significantly improved using ANN-MLP. Under these conditions, the recognition (90.1%) and prediction (81.1%) abilities were achieved. The diagnostic value of the data obtained by one-dimensional GC–ITMS were compared with those generated by two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS), allowing a comprehensive analysis of olive oil volatiles.     

Geographical origin discrimination of wheat kernel and white flour using near-infrared reflectance spectroscopy fingerprinting coupled with chemometrics

Wheat kernel and flour with three genotypes across 4 years procured from three different geographical areas of China were analysed using near-infrared reflectance (NIR) spectroscopy coupled with chemometrics to better classify wheat according to the origin, production year and genotypes, respectively. For this purpose, principle component analysis-linear discriminant analysis and multi-way anova were applied to the NIR data. The best classification percentages were obtained for flour matrix both for geographical origin and production years with the correct percentages of 100% and 73%, respectively. For genotypes, wheat whole kernel showed better classification percentage (98.2%). All the samples were validated using external validation procedure and the obtained percentages were found satisfactory with the average prediction abilities of >85% in all regions indicating the suitability of the developed model. Multivariate anova showed that NIR fingerprints of wheat kernels and flours were significantly influenced by regions, years, genotypes and their interactions. In conclusion, white flour showed better performance in discriminating the geographical origin as compared to wheat whole kernel.     

Characterisation of the geographical origin of Western Greek virgin olive oils based on instrumental and multivariate statistical analysis

In this work, measurements of free acidity, peroxide content, spectrophotometric parameters, chlorophyll content, phenol, sterol, fatty acid and triacylglycerol composition, were carried out on samples of virgin olive oils (VOOs) coming from four different Greek Ionian islands, i.e. Zakynthos, Kefalonia, Lefkada and Kerkyra. An analysis of variance (ANOVA) highlighted statistically significant differences (p < 0.01) in the values of 26 analytical parameters amongst the VOOs produced in the four different geographical regions but a post-hoc test showed that no variable was able to distinguish all four origins. Analogously, a Principal Component Analysis (PCA) showed a modest grouping of VOOs according to geographical origin except for Kerkyra samples which were more distinct from others. Applying discriminant function analysis (DFA) a good separation of the four geographical groups was achieved with classification and prediction abilities equal to 97.7% and 95.3%, respectively. Moreover, the analysis of the standardised coefficients showed that the fatty acids and triacylglycerols were the most discriminant variables. This last outcome was confirmed by comparison of the prediction performances obtained applying DFA on four subdatasets containing fatty acids (69.8%), triacylglycerols (76.7%), sterols (62.8%), and remaining parameters (65.1%) together, respectively. As the results showed, the multidisciplinary approach that combines different types of analytical determinations improved the discrimination of geographical origin for Greek virgin olive oils.     

基于质地特性的羊肉产地鉴别

为实现羊肉产地快速、客观的鉴别,本文利用质地特性对甘肃山丹、东乡、兰州、靖远4个不同产地羊肉进行分析,并研究其持水性及质地特性的变化规律。对不同产地的羊肉进行定性区分和定量分析,结果表明:不同地域羊肉具有其独特的质地特性,靖远羊肉的嫩度和弹性最高;兰州羊肉的弹性、嫩度、内聚性最低,其持水性和硬度最高;羊肉的质地特征来看,尤以靖远羊肉品质为佳。采用质地特性参数结合多元统计分析(主成分分析、典则判别分析和线性判别分析)对羊肉产地进行鉴别,结果发现,主成分分析和典则判别分析均可定性识别不同产地羊肉;线性判别分析建立的羊肉地域判别模型,对四个产地羊肉的正确识别率达到70%,利用质地特性在羊肉产地的鉴别中具有可行性。此研究结果为羊肉产地的鉴别提供参考。     

Classification of extra virgin olive oils according to their genetic variety using linear discriminant analysis of sterol profiles established by ultra-performance liquid chromatography with mass spectrometry detection

A method to classify extra virgin olive oils (EVOOs) according to their genetic variety using sterol profiles obtained by ultra-performance liquid chromatography (UPLC) with atmospheric pressure chemical ionization mass spectrometry (APCI-MS) detection has been developed. The optimal separation conditions were obtained using a gradient acetonitrile/water (0.01% acetic acid) at a flow rate of 0.8 mL min^− 1 and 10 °C. Linear discriminant analysis (LDA) models were constructed with the 11 UPLC-APCI-MS sterol peaks taken from the selective ion recording mode chromatograms. Ratios of the peak areas selected by pairs were used as predictors. With the sequential application of two LDA models and using a 95% probability, the EVOO samples belonging to seven genetic varieties mainly produced at La Comunitat Valenciana, Spain, were correctly classified with a prediction capability higher than 97%.     

The Correlation of Inorganic Anion Contents in Rice and Its Soils Based on Four Geographical Origin

The determination of the geographical origin of foodstuffs is becoming of increasing interest to consumers and producers since it may be used as a criterion to certify quality and typicality. The correlation of inorganic anion contents in rice and its origin soils was studied in this paper, and the inorganic anion contents were used to identification the rice geographical origin. The contents of F^?, Cl^?, NO₂ ^?, NO₃ ^?, and SO₄ ^2? in rice samples and soil samples from four provinces of China were analyzed by ion chromatography. The result of variance analysis and correlation analysis demonstrated that there is significant difference in the contents for F^?, Cl^?, NO₂ ^?, NO₃ ^?, and SO₄ ^2? in the rice samples from four provinces, and the anions in rice are closely connected with the anions in soil. The predictions of geographic origin made by linear discriminant analysis (LDA) based on anions gave an overall correct classification rate of 100 % and cross-validation rate of 96.9 %. The correct rate of Q-type hierarchical cluster analysis (Q-type CA) was 81.3 %. These results demonstrate the usefulness of multi-anion fingerprints as indicators for authenticating the geographical origin of the four famous brand rices.     

Green tea – A potential preservative for extending the shelf life of fresh mutton at ambient temperature (25 ± 2 °C)

This investigation was taken up to evaluate the feasibility of using green tea (GT) to extend the shelf life of fresh mutton, at ambient storage conditions (25 ± 2 °C and 85 ± 5% RH). The ethanolic extract of GT (GTE) was found to significantly inhibit (P < 0.01) spoilage microflora, including certain pathogens of acidulant treated mutton (pH 3.8) for up to 4 days. Application of GTE did not cause any deleterious change in sensorial and physical quality and the mutton was acceptable for up to 4 days. While the control samples showed initial signs of spoilage between 20 and 24 h and registered an increase in free fatty acids (FFA) from 1.24 g to 4.1 g/100 g lipid and biogenic amine index (BAI) from 0.27 mg to 4.63 mg/100 g mutton, at the end of two days of storage, the GTE treated sample showed FFA levels of 1.5 g/100 g lipid and BAI of 0.25 mg/100 g mutton at the end of the 4 days. GTE treatment could be effectively used to extend the shelf life of fresh mutton for up to 4 days in Indian climatic conditions, since it significantly (P < 0.01) inhibits the formation of these lipolytic and proteolytic degradation products, which are responsible for sensorial spoilage.     

我国多产区羊肉中无机元素含量特征及其在溯源研究中的应用

目的 研究我国多个产区所产羊肉中多种无机元素含量的特征,探索元素含量差异在羊肉产地溯源研究中的应用方法。方法 收集在地域和喂养方式上具有一定代表性的产区所产羊肉样本,采用电感耦合等离子体质谱检测羊肉中无机元素含量。结合方差分析、主成分分析、聚类分析、判别分析多种统计方法进行分析。结果 在检测的24种元素中,有18种元素在产地间存在显著差异。通过主成分分析确定了5个主成分,可以大致将4个产地的样品区分开。通过聚类分析,可以将牧区的样品大致与农饲区样品区分开。通过判别分析建立判别函数,对牧区与农饲区样品的交叉判别正确率为82.0%。结论 研究表明无机元素可以作为羊肉产地溯源的指标。初步建立了国内4个羊肉产地的溯源模型，为国产羊肉产地溯源研究提供依据。     

Age and nutrition influence the concentrations of three branched chain fatty acids in sheep fat from Australian abattoirs

The characteristic mutton odour, associated with the cooked meat of older sheep, can be problematic for some consumers who find the odour disagreeable. Branch chain fatty acids (BCFAs) are considered to be the main determinants of mutton odour. In this study, the aim was to identify the factors influencing the BCFA content of animals at abattoirs in Australia. Samples of subcutaneous fat from over the chump (gluteus medius) were collected from 533 sheep carcasses at abattoirs in New South Wales, Victoria and Western Australia. The carcasses were from sheep differing in age, gender, breed and nutrition. The concentrations of three branched chain fatty acids (BCFAs); namely, 4-methyloctanoic (MOA), 4-ethyloctanoic (EOA) and 4-methylnonanoic acids (MNA), were determined. Statistical modelling showed that, with pre-slaughter nutrition in the model as a random term, BCFA concentrations could be used for discriminating the age of sheep. Fat samples from lamb carcasses had lower MOA and EOA concentrations and a higher concentration of MNA in comparison to hogget and mutton (P < 0.05). When nutrition was excluded as a random effect from the statistical model, the MOA and MNA concentrations did not differentiate between lamb, hogget and mutton whereas, for EOA, lamb had a lower concentration than mutton (P < 0.05) with hogget intermediate. An interaction existed between age and gender (P < 0.05) where female lambs had lower EOA concentrations relative to the mutton but not for castrates.     

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.     

Effect of glucose on the stability of fresh mutton during storage at ambient temperature (25 ± 2 °C)

The feasibility of using d-glucose to extend the shelf life of fresh mutton at 25 ± 2 °C was investigated. Aqueous solutions of 5% d-glucose (w/v) containing 1000 ppm potassium sorbate, when applied on acidulant-treated mutton, are able to inhibit spoilage by delaying proteolytic activity, as seen by significantly lower (P < 0.01) levels of biogenic amines until 4 days of storage. Though there was no significant change in standard plate count during storage, as compared with a 1 day control, Enterobacteria were significantly (P < 0.01) inhibited. Cadaverine, the marker for threshold level of spoilage, was absent up to 4 days. Measurement of glucose retention revealed that 0.25% (0.25 g/100 g mutton) was still available at the end of 4 days, when no spoilage had set in, though there was a slight fermented odour. Thus, the investigation shows that, surface treatment with glucose helps to extend the shelf life of fresh mutton up to 3 days at 25 ± 2 °C.     

大米理化指标指纹在产地溯源的探究

为了比较不同地域来源大米理化指标含量差异,分析各理化指标对大米产地的判别效果,探讨理化指标指纹分析技术对大米产地鉴别的可行性。通过对来自查哈阳、建三江和五常3个产区89份大米样品的蛋白质、直链淀粉、脂肪和灰分的含量进行测定,对数据进行单因素方差分析、多重比较分析和判别分析。结果表明:不同产地大米的理化指标有显著差异。建三江大米样品的蛋白质含量最高,直链淀粉和脂肪含量最低;五常大米样品的脂肪含量最高,蛋白质和灰分含量最低;查哈阳大米样品的各指标含量均处于中间状态。进一步利用蛋白质、直链淀粉、脂肪和灰分的含量对大米产地进行判别分析,交叉检验正确判别率为95.5%,说明不同地域来源的大米有其独特的品质特征,理化指标指纹分析是鉴别大米产地的一种潜在技术。     

Evaluation of an artificial olfactory system for grain quality discrimination

A commercially available Cyranose-320™ conducting polymer-based electronic nose system was used to analyze the headspace from stored barley samples. Three types of barley samples were analyzed, namely, clean barley, naturally Fusarium infected barley and Fusarium inoculated clean barley. The barley samples were stored at moisture contents of 13, 18, 20 and 25 g of water/100 g sample. The raw signals obtained from the electronic nose system were pre-processed by various signal-processing techniques to extract area-based features. Principal component analysis was subsequently performed on the processed signals to further reduce the dimensionalities. Classification models using linear (LDA) and quadratic discriminant analyses (QDA) were developed using the extracted features. The performance of the developed models was validated using leave-1-out cross validation and bootstrapping method. The models classified the barley samples stored into two groups based on the ergosterol content, i.e., “acceptable” (ergosterol content <3.0 μg/g) and “unacceptable” (ergosterol content ?3.0 μg/g). Overall, the total maximum classification accuracy obtained was 86.8% by both LDA and QDA when leave-1-out cross-validation was used. By bootstrapping validation the maximum total classification accuracy obtained was 86.4% and 86.1% respectively, by QDA and LDA. The study proves that there is potential in using an electronic nose system for indicating mold spoilage in stored grains, and necessitates future studies in this direction.     

应用红外光谱结合化学计量学方法检测阿拉伯胶产地和蛋白质含量

研究利用傅里叶红外光谱结合化学计量学方法来实现对苏丹阿拉伯胶的产地和蛋白质含量的快速无损检测的可行性。采集自6?个不同的产地,每个产地12?个,总计72?个阿拉伯胶样本,作为研究对象,运用线性判别分析（linear discriminant analysis,LDA）和反向区间偏最小二乘（backward interval partial least squares,Bi-PLS）法分别实现对苏丹阿拉伯胶的产地区分和蛋白质含量检测。结果表明,当主成分数为6时,LDA对样本的训练集（48?个样本）和预测集（24?个样本）的识别率都为100%。Bi-PLS法回归联合20?个光谱子区间中的4?个子区间得到最佳的蛋白质预测模型,其预测集相关系数为0.937?3,均方根误差为0.173%。因此,利用傅里叶红外光谱结合化学计量学方法可实现对苏丹阿拉伯胶的产地以及蛋白质的含量的快速无损检测。     

Characterisation of Serbian unifloral honeys according to their physicochemical parameters

The characterisation of three unifloral Serbian honeys (acacia, sunflower and linden) was carried out based on some common physicochemical parameters (water content, electrical conductivity, free acidity, optical rotation and pH). A total of 201 honey samples, collected during the 2009 harvesting season, were analysed. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to highlight the data structure and to find the relationships between the physicochemical parameters and the botanical origin of honey. The variables that best discriminated the samples were electrical conductivity (ranging from 0.10 to 0.76 mS/cm), free acidity (ranging from 7.80 to 42.70 meq/kg) and pH (ranging from 3.17 to 5.85). LDA resulted in a classification model with a high predictive power, allowing further assessment of unknown samples of the three unifloral honeys. Determination of geographic origin of acacia honey samples based on physicochemical properties and chemometrics was attempted.     

Thermal imaging for detecting fungal infection in stored wheat

The feasibility of using an infrared thermal imaging system to identify the fungal infection in stored wheat was studied. Thermal images of bulk wheat grains infected by Aspergillus glaucus group, Aspergillus niger van Tieghem and Penicillium spp. were obtained using an un-cooled focal planar array type infrared thermal camera after heating grain for 180 s with a plate heater placed 10 mm above the grain and maintained at 90 °C, and then cooling in ambient air for 30 s. In total, twelve temperature features were derived from heated and cooled wheat and four-way and pair-wise classification models were developed by linear and quadratic discriminant analyses (LDA and QDA). Leave-one-out and bootstrapping methods were used to validate the developed classification models. Pair-wise LDA and QDA classification models gave a maximum accuracy of 100% for healthy samples and more than 97% and 96% for infected samples, respectively. Four-way LDA and QDA classification models yielded relatively low classification accuracies for fungus-infected samples due to the non-significant changes in the temperature features between grain samples infected with different species of fungi.