Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

葡萄籽超微粉添加对曲奇饼干香气的影响

将葡萄籽超微粉碎并添到曲奇饼干中,探究其对曲奇饼干香气成分及感官品质的影响。采用气相色谱-质谱联用仪分别检测了普通葡萄籽粉、葡萄籽超微粉及添加超微粉前后曲奇饼干的香气成分,并结合偏最小二乘判别分析法研究葡萄籽超微粉对曲奇饼干香气成分的影响。结果显示,超微粉碎后香气物质的种类虽未发生明显的改变,但香气成分的相对含量显著增加。葡萄籽超微粉添加比例为5%时,烘烤香比较浓郁。当添加比例达到10%时,曲奇饼干整体风味较为复杂,包括苦杏仁味、香蕉味、水果味/青草味、面包味以及坚果味。采用9点享乐法感官品评结果可以看出,葡萄籽的添加可以为饼干带来可感知的变化,添加比例为5%时,香气得分最高。葡萄籽超微粉会给曲奇饼干香气带来积极的影响,添加比例在10%以内均可被消费者接受。     

Grading and color evolution of apples using RGB and hyperspectral imaging vision cameras

The potential of RGB digital imaging and hyperspectral imaging (900–1700 nm) was evaluated for discriminating maturity level in apples under different storage conditions along the shelf-life. Segmentation, preprocessing and partial least squares-discriminant analysis (PLS-DA) were used for hyperspectral data analysis, while illumination correction, dimensionality reduction and linear discriminant analysis (LDA) were used for RGB data analysis. The results showed that hyperspectral discrimination classified different storage regimes better than RGB, with an overall success rate of 95.83%. In addition, color evolution of apples during shelf-life under different storage regimes was modeled using RGB zero and first order regression models, fitting better to a first order kinetic model.     

Near-infrared spectroscopy to assist authentication and labeling of Asiago d’allevo cheese

This study investigated the possibility of using near-infrared spectroscopy (NIRS) for the authentication of Asiago d’allevo, a protected designation of origin cheese from northern Italy. Latent variable models applied on spectral data were developed and used to estimate several chemical properties and to classify the available dataset according to the location and management of the cheesemaking factory (lowland and alpine), the ripening age (6, 12, 18 and 36 months), the altitude of milk production (low, medium, medium–high and high), and the period of the year of the cheese production (May, July and September). The variable importance in projection index was used to identify the most informative spectral regions for discrimination. Results showed that NIR spectra can be used both to accurately estimate several chemical properties and to classify the samples according to the different experimental conditions under investigation with the same discrimination capacity provided by traditional chemical analysis.     

Electronic nose and GC–MS analysis of volatile compounds in Tuber magnatum Pico: Evaluation of different storage conditions

The characteristic aromatic composition of white truffles (Tuber magnatum Pico) determines its culinary and commercial value. However modifications of truffle organoleptic proprieties occur during preservation. A study of headspace of white truffles by using Electronic nose (E-nose), gas chromatography–mass spectrometry (GC–MS) and sensory analyses was performed. Truffles were stored at different conditions for 7 days: +4 and +8 °C wrapped in blotting paper or covered by rice or none of the above. Headspace E-nose measurements and sensory analyses were performed each day. Statistical multivariate analysis of the data showed the capability of E-nose to predict sensorial analysis scores and to monitor aroma profile changes during storage. Truffle’s volatile molecules were also extracted by headspace solid phase microextraction technique and separated and identified by GC–MS. Partial Components Analysis of data was performed. E-nose and GC–MS results were in agreement and showed that truffle storage in paper at +8 °C seemed to be the best storage condition.     

4种不同酵母酿造杨梅酒的风味特征差异分析

目的 探究3种商业酿酒酵母(DV10、D254、X16)及1种从杨梅醪中筛选的原生酵母(CCTCC M2015627)酿造杨梅酒的风味特征差异。方法 通过顶空固相微萃取-气相色谱-质谱法(headspace solid-phase micro-extraction-gas chromatography-mass spectrometry,HS-SPME-GC-MS)检测不同酵母发酵杨梅酒的挥发性香气物质,通过计算相对气味活性值(relative odor activity value,ROAV)确定重要香气活性化合物,根据变量重要性投影(variable importance in projection,VIP)值筛选关键差异特征呈香物质。结果 4种酵母发酵杨梅酒共鉴定出107种挥发性成分,其中原生酵母发酵的杨梅果酒香气物质种类总数最多,为93种,X16杨梅酒香气物质类别丰富度最低,为62种,DV10及D254杨梅酒产香物质类别丰富度居中。DV10杨梅酒香气物质总含量最高,且ROAV≥0.1的香气活性成分最多,为17种。ROAV≥1的物质为异丁酸乙酯、丁酸乙酯、乙酸异戊酯、正己酸乙酯,...     

基于可见/近红外光谱技术的橘小实蝇侵染柑橘检测研究

目的 使用可见/近红外光谱技术实施橘小实蝇侵染柑橘不同时期的无损检测。方法 研究选取人工制备的不同侵染时期的柑橘样本作为研究对象,利用搭建的可见/近红外光谱系统测量的光谱信息结合人工标定的侵染时期,对原始光谱进行了5种预处理,采用竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)、连续投影算法(successive projections algorithm, SPA)两类方法提取反映侵染柑橘时期变化的光谱特征波长,应用偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)建立基于特征波长光谱的柑橘侵染时期分类模型,对比分析不同光谱预处理方法的模型分类效果。结果 原始光谱经多元散射校正(multiplicative scatter correction, MSC)预处理的模型分类效果最佳,分别经CARS方法和SPA方法优选出了34和16个光谱特征波长。采用MSC-CARS-PLS-DA方法构建的模型分类效果最好,总准确率、假阳率分别为96.8%和0.0...     

Quantification and statistical significance analysis of group separation in NMR-based metabonomics studies

Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures makes it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported by a standard and rigorous assessment of whether or not the cluster separation is statistically significant. Here quantification and statistical significance of separation of group centroids in PCA and PLS-DA scores plots are considered. The Mahalanobis distance is used to quantify the distance between group centroids, and the two-sample Hotelling's T² test is computed for the data, related to an F-statistic, and then an F-test is applied to determine if the cluster separation is statistically significant. We demonstrate the value of this approach using four datasets containing various degrees of separation, ranging from groups that had no apparent visual cluster separation to groups that had no visual cluster overlap. Widespread adoption of such concrete metrics to quantify and evaluate the statistical significance of PCA and PLS-DA cluster separation would help standardize reporting of metabonomics data.     

Geographical origin classification of olive oils by PTR-MS

The volatile compositions of 192 olive oil samples from five different European countries were investigated by PTR-MS sample headspace analysis. The mass spectra of all samples showed many masses with high abundances, indicating the complex VOC composition of olive oil. Three different PLS-DA models were fitted to the data to classify samples into ‘country’, ‘region’ and ‘district’ of origin, respectively. Correct classification rates were assessed by cross-validation. The first fitted model produced an 86% success rate in classifying the samples into their country of origin. The second model, which was fitted to the Italian oils only, also demonstrated satisfactory results, with 74% of samples successfully classified into region of origin. The third model, classifying the Italian samples into district of origin, yielded a success rate of only 52%. This lower success rate might be due to either the small class set, or to genuine similarities between olive oil VOC compositions on this tight scale.