Authentication of protected designation of origin artichokes by spectroscopy methods

Incidence of foliar treatments and geographical origin on the geochemical fingerprints of leaves and fruits in olive growing

Recently, food quality and safety has become of great interest, with a consequent demand for geographical identification of agri-food products and eco-friendly agricultural practices. In this study geochemical analyses of soils, leaves and olives from two areas in the Emilia-Romagna Region (Italy), Montiano and San Lazzaro were performed aiming at identifying geochemical fingerprints able to (1) univocally determine the locality of provenance and (2) the effect of different foliar treatments (control, dimethoate, and alternating of natural zeolitite and dimethoate in MN; Spinosad + Spyntor fly, natural zeolitite and NH₄⁺-enriched zeolitite in SL). PCA and PLS-DA (including VIP analysis) were used to discriminate between localities and different treatments. Bioaccumulation and Translocation Coefficients (BA and TC) were studied to evaluate differences in the uptake of trace elements by plants. The PCA performed on soil data highlighted a total variance of 88.81%, allowing a good distinction between the two sites. Leaves and olives PCA showed that using trace elements it is possible to discriminate different foliar treatments (total variance: 95.64% and 91.08% in MN; 71.31% and 85.33% in SL of leaves and olives, respectively) better than the identification of their geographical origin (87.46% of leaves and 83.50% of total variance of olives). PLS-DA of all samples gave the largest contribution to the discrimination of different treatments and geographical identification. Among all elements, only Lu and Hf were able to correlate soil, leaf, and olive for geographical identification through VIP analyses, but also Rb and Sr were significant in the plant uptake (BA and TC). For the discrimination of different foliar treatments, Sm and Dy were identified in MN site, whereas Rb, Zr, La and Th correlated leaves and olives from SL. Based on trace element analyses, it can be put forward that (1) the geographical origin could be discriminated and (2) different foliar treatments applied for crop protection can be recognized, which means, reversing the reasoning that each farmer can develop a method to pinpoint his own product.

Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process

The theaflavin-to-thearubigin ratio (TF/TR) is an important parameter for evaluating the degree of fermentation and quality characteristics of Congou black tea. Near infrared (NIR) spectroscopy, one of the most promising techniques for evaluating large-scale tea processing quality, in association with chemometrics, can be used as a selection tool when a fast determination of the requested parameters is required. The aim of this work is to develop a unique model for the determination of TF/TR. First, 11 key wavelength variables were screened by synergy interval partial least-squares regression (SI-PLS) and competitive adaptive reweighted sampling (CARS). Based on these characteristic variables, a new extreme learning machine (ELM) combined with an adaptive boosting (ADABOOST) algorithm (ELM-ADABOOST) was applied to construct the nonlinear prediction model for TF/TR, and an independent external set was used for the validation. A determinate coefficient (R_p²) of 0.893, root mean square error of prediction (RMSEP) of 0.0044, RSD below 10%, and RPD above 3 were acquired in the prediction model. These results demonstrate that NIR can be used to rapidly determine the TF/TR value during fermentation, and it effectively simplify the model and improve the prediction accuracy when combined with the SI-CARS variable.

Rare Earth Element Labeling as a Tool for Assuring the Origin of Eggs and Poultry Products

Laying hens were fed terbium and thulium supplemented feed in order to introduce a distinctive rare earth element pattern that allows discrimination of labeled from unlabeled poultry products. Samples of egg yolk, egg shells, meat, bones, liver, blood, and feces were analyzed using either conventional or laser ablation inductively coupled plasma mass spectrometry. Already after a short time of administering supplemented feed, terbium and thulium enrichment could be unambiguously detected in the products, while absolute terbium and thulium contents remained low enough to ensure safety for the customer. This method could potentially be applied to specifically label foodstuffs produced in certain regions or under certain conditions, in order to ensure food authenticity.

Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods

Fermentation is the key process to produce the special color of congou black tea. The machine vision technology is applied to detect the color space changes of black tea's color in RGB, Lab and HSV, and to find out its relevance to black tea's fermentation quality. And then the color feature parameter is used as input to establish physicochemical indexes (TFs, TRs, and TBs) and sensory features' linear and non-linear quantitative evaluation model. Results reveal that color features are significantly correlated to quality indices. Compared with the other two color models (RGB and HSV), CIE Lab model can better reflect the dynamic variation features of quality indices and foliage color information of black tea. The predictability of non-linear models (RF and SVM) is superior to PLS linear model, while RF model presents a slight advantage over the classic SVM model since RF model can better represent the quantitative analytical relationship between image information and quality indices. This research has proved that computer image color features and non-linear method can be used to quantitatively evaluate the changes of quality indices (e.g. sensory quality) and the pigment during black tea's fermentation. Besides, the test is simple, fast, and nondestructive.

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method

Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L^*) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools

Tea is one of the three greatest beverages in the world. In China, green tea has the largest consumption, and needle-shaped green tea, such as Maofeng tea and Sparrow Tongue tea, accounts for more than 40% of green tea (Zhu et al., 2017). The appearance of green tea is one of the important indexes during the evaluation of green tea quality. Especially in market transactions, the price of tea is usually determined by its appearance (Zhou et al., 2012). Human sensory evaluation is usually conducted by experts, and is also easily affected by various factors such as light, experience, psychological and visual factors. In the meantime, people may distinguish the slight differences between similar colors or textures, but the specific levels of the tea are hard to determine (Chen et al., 2008). As human description of color and texture is qualitative, it is hard to evaluate the sensory quality accurately, in a standard manner, and objectively. Color is an important visual property of a computer image (Xie et al., 2014; Khulal et al., 2016); texture is a visual performance of image grayscale and color changing with spatial positions, which can be used to describe the roughness and directivity of the surface of an object (Sanaeifar et al., 2016). There are already researchers who have used computer visual image technologies to identify the varieties, levels, and origins of tea (Chen et al., 2008; Xie et al., 2014; Zhu et al., 2017). Most of their research targets are crush, tear, and curl (CTC) red (green) broken tea, curly green tea (Bilochun tea), and flat-typed green tea (West Lake Dragon-well green tea) as the information sources. However, the target of the above research is to establish a qualitative evaluation method on tea quality (Fu et al., 2013). There is little literature on the sensory evaluation of the appearance quality of needle-shaped green tea, especially research on a quantitative evaluation model (Zhou et al., 2012; Zhu et al., 2017).

Prediction of black tea fermentation quality indices using NIRS and nonlinear tools

Catechin content, the ratio of tea polyphenols and free amino acids (TP/FAA), as well as the ratio of theaflavins and thearubigins (TFs/TRs) are important biochemical indicators to evaluate fermentation quality. To achieve rapid determination of such biochemical indicators, synergy interval partial least square and extreme learning machine combined with an adaptive boosting algorithm, Si-ELM-AdaBoost algorithm, were used to establish quantitative analysis models between near infrared spectroscopy (NIRS) and catechin content and between TFs/TRs and TP/FAA, respectively. The results showed that prediction performance of the Si-ELM-AdaBoost mixed algorithm is superior than that of other models. The prediction results with root-mean-square error of prediction ranged from 0.006 to 0.563, the ratio performance deviation values exceeded 2.5, and predictive correlation coefficient values exceeded 0.9 in the prediction model of each biochemical indicator. NIRS combined with Si-ELM-AdaBoost mixed algorithm could be utilized for online monitoring of black tea fermentation. Meanwhile, the AdaBoost algorithm effectively improved the accuracy of the ELM model and could better approach the nonlinear continuous function.