Application of visible/near infrared spectroscopy and chemometric calibrations for variety discrimination of instant milk teas

Effects of Protein on Green Tea Quality in a Milk-Tea Model during Heat Treatment: Antioxidant Activity, Foaming Properties, and Unbound Small-molecule Metabolome

Tea drinks/beverage has a long history and milk is often added to enhance its taste and nutritional value, whereas the interaction between the tea bioactive compounds with proteins has not been systematically investigated. In this study, a milk-tea model was prepared by mixing green tea solution with milk and then heated at 100°C for 15 min. The milk tea was then measured using biochemical assay, antioxidant detection kit, microscopy as well as HPLC-QTOF-MS/MS after ultrafiltration. The study found that as the concentration of milk protein increased in the milk-tea system, the total phenol-protein binding rate raised from 19.63% to 51.08%, which led to a decrease in free polyphenol content. This decrease of polyphenol was also revealed in the antioxidant capacity, including 2,2-diphenyl-1-picrylhydrazyl radical scavenging ability and ferric ion reducing antioxidant power, in a dose-dependent manner. Untargeted metabolomics results revealed that the majority of small-molecule compounds/polyphenols in tea, such as epigallocatechin gallate, (-)-epicatechin gallate, and Catechin 5,7,-di-O-gallate, bound to milk proteins and were removed by ultrafiltration after addition of milk and heat treatment. The SDS-PAGE and Native-PAGE results further indicated that small molecule compounds in tea formed covalent and non-covalent complexes by binding to milk proteins. All above results partially explained that milk proteins form conjugates with tea small-molecule compounds. Consistently, the particle size of the tea-milk system increased as the tea concentration increased, but the polymer dispersity index decreased, indicating a more uniform molecular weight distribution of the particles in the system. Addition of milk protein enhanced foam ability in the milk-tea system but reduced foam stability. In summary, our findings suggest that the proportion of milk added to tea infusion needs to be considered to maintain the quality of milk-tea from multiple perspectives, including stability, nutritional quality and antioxidant activity.

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

Developments and Challenges in Online NIR Spectroscopy for Meat Processing

Meat and meat products are popular foods due to their balanced nutritional nature and their availability in a variety of forms. In recent years, due to an increase in the consumer awareness regarding product quality and authenticity of food, rapid and effective quality control systems have been sought by meat industries. Near-Infrared (NIR) spectroscopy has been identified as a fast and cost-effective tool for estimating various meat quality parameters as well as detecting adulteration. This review focusses on the on/inline application of single and multiprobe NIR spectroscopy for the analysis of meat and meat products starting from the year 1996 to 2017. The article gives a brief description about the theory of NIR spectroscopy followed by its application for meat and meat products analysis. A detailed discussion is provided on the various studies regarding applications of NIR spectroscopy and specifically for on/inline monitoring along with their advantages and disadvantages. Additionally, a brief description has been given about the various chemometric techniques utilized in the mentioned studies. Finally, it discusses challenges encountered and future prospects of the technology. It is concluded that, advancements in the fields of NIR spectroscopy and chemometrics have immensely increased the potential of the technology as a reliable on/inline monitoring tool for the meat industry.

Applications of near-infrared spectroscopy in food safety evaluation and control: a review of recent research advances

Food safety is a critical public concern, and has drawn great attention in society. Consequently, developments of rapid, robust, and accurate methods and techniques for food safety evaluation and control are required. As a nondestructive and convenient tool, near-infrared spectroscopy (NIRS) has been widely shown to be a promising technique for food safety inspection and control due to its huge advantages of speed, noninvasive measurement, ease of use, and minimal sample preparation requirement. This review presents the fundamentals of NIRS and focuses on recent advances in its applications, during the last 10 years of food safety control, in meat, fish and fishery products, edible oils, milk and dairy products, grains and grain products, fruits and vegetables, and others. Based upon these applications, it can be demonstrated that NIRS, combined with chemometric methods, is a powerful tool for food safety surveillance and for the elimination of the occurrence of food safety problems. Some disadvantages that need to be solved or investigated with regard to the further development of NIRS are also discussed.

Combination of the Manifold Dimensionality Reduction Methods with Least Squares Support vector machines for Classifying the Species of Sorghum Seeds

This study was carried out for rapid and noninvasive determination of the class of sorghum species by using the manifold dimensionality reduction (MDR) method and the nonlinear regression method of least squares support vector machines (LS-SVM) combing with the mid-infrared spectroscopy (MIRS) techniques. The methods of Durbin and Run test of augmented partial residual plot (APaRP) were performed to diagnose the nonlinearity of the raw spectral data. The nonlinear MDR methods of isometric feature mapping (ISOMAP), local linear embedding, laplacian eigenmaps and local tangent space alignment, as well as the linear MDR methods of principle component analysis and metric multidimensional scaling were employed to extract the feature variables. The extracted characteristic variables were utilized as the input of LS-SVM and established the relationship between the spectra and the target attributes. The mean average precision (MAP) scores and prediction accuracy were respectively used to evaluate the performance of models. The prediction results showed that the ISOMAP-LS-SVM model obtained the best classification performance, where the MAP scores and prediction accuracy were 0.947 and 92.86%, respectively. It can be concluded that the ISOMAP-LS-SVM model combined with the MIRS technique has the potential of classifying the species of sorghum in a reasonable accuracy.

Classification of washing powder brands using near-infrared spectroscopy combined with chemometric calibrations

In this study, near-infrared (NIR) spectroscopy is applied for rapid and objective classification of 5 different brands of washing powder. Chemometric calibrations including partial least square discriminant analysis (PLS-DA), back propagation neural network (BP-NN) and least square support vector machine (LS-SVM) are investigated and compared to achieve an optimal result. Firstly, principal component analysis (PCA) is conducted to visualize the difference among washing powder samples of different brands and principal components (PCs) are extracted as inputs of BP-NN and LS-SVM models. The number of PCs and parameters of such models are optimized via cross validation. In experimental studies, a total of 225 spectra of washing powder samples (45 samples for each brand) were used to build models and 75 spectra of washing powder samples (15 samples for each brand) were used as the validation set to evaluate the performance of developed models. As for the comparison of the three investigated models, both BP-NN model and LS-SVM model successfully classified all samples in validation set according to their brands. However, the PLS-DA model failed to achieve 100% of classification accuracy. The results obtained in this investigation demonstrate that NIR spectroscopy combined with chemometric calibrations including BP-NN and LS-SVM can be successfully utilized to classify the brands of washing powder.

Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis

A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

Pre-visual diagnostics of phosphorus deficiency in mini-cucumber plants using near-infrared reflectance spectroscopy

The morphological symptoms of phosphorus (P) deficiency in the leaves of mini-cucumber plants at early stages of development have features similar to that of early stage development in healthy plants. That similarity may lead to inappropriate visual diagnostics of phosphorus deficiency in analyzed samples. Because the differences in spectral properties of leaf tissues between phosphorus-deficient and healthy plants can be demonstrated, the feasibility of using near-infrared (NIR) spectroscopy for rapid and nondestructive diagnostics of phosphorus deficiency in mini-cucumber plants was investigated. Leaf reflection spectra in the wavelength range of 10,000-4000 cm(-1) were measured before the appearance of morphological changes caused by phosphorus deficiency. Least-squares support vector machine (LS-SVM), a method for recognizing patterns, was applied to identify phosphorus-deficient plants. Parameters (γ, σ(2)) of LS-SVM were optimized by cross-validation, and several conventional, two-class classification methods such as linear discrimination analysis and K-nearest neighbors were also used comparatively for identification. Identification rates in excess of 86% were achieved with the LS-SVM model for both the training set and the prediction set. The overall results indicated that NIR spectra combined with LS-SVM could be used efficiently for pre-visual diagnostics of phosphorus deficiency in mini-cucumber plants.