Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy

Electronic nose application for detecting different odorants in source water: Possibility and scenario

The problem of taste and odor (T&O) in drinking water is a widespread societal concern and highlights substantial challenges related to the detection and evaluation of odor in water. In this study, the portable electronic nose PEN3, which is equipped with ten different heated metal sensors, was applied to analyze its applicability, feasibility and application scenarios for the detection of typical odorants, such as 2-methylisobornel (2-MIB), geosmin (GSM), β-cyclocitral, β-ionone, and other T&O compounds in source water, while avoiding uncertainties and instability related to manual inspection. All the T&O compounds could be effectively differentiated by principal component analysis (PCA). Linear discriminant analysis (LDA) showed that the odors varied greatly between different samples and could be effectively distinguished. As the odorant concentration increased, the sensor response intensity of the primary identification sensors R6 and R8 increased with a significant positive correlation. For Microcystis aeruginosa, an algae that produces odorants, PCA could distinguish the odors of algae at a series of densities at different concentrations. The responses of R10 showed a significant increase with increasing algae density, implying the production of more aliphatic hydrocarbons and other odor compounds. The results indicated that the electronic nose could provide a promising alternative to traditional unstable and complex detection methods for the detection of odorous substances in surface water and early warning of odor events. This study aimed to provide technical support for rapid monitoring and early warning of odorants in source water management.

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction of complex unknown relations between the input data and desired output parameters. Recently, soft sensors have been applicable in predicting the prices and vintages of New Zealand Pinot noir wines based on chemical parameters. However, the previous sample size did not adequately represent the diversity of provenances, vintages, and price points across commercially available New Zealand Pinot noir wines. Consequently, a representative sample of 39 commercially available New Zealand Pinot noir wines from diverse provenances, vintages, and price points were selected. Literature has shown that wine phenolic compounds strongly correlated with wine provenances, vintages and price points, which could be used as input data for developing soft sensors. Due to the significance of these phenolic compounds, chemical parameters, including phenolic compounds and pH, were collected using UV-Vis visible spectrophotometry and a pH meter. The soft sensor utilising Naive Bayes (belongs to ML) was designed to predict Pinot noir wines' provenances (regions of origin) based on six chemical parameters with the prediction accuracy of over 75%. Soft sensors based on decision trees (within ML) could predict Pinot noir wines' vintages and price points with prediction accuracies of over 75% based on six chemical parameters. These predictions were based on the same collected six chemical parameters as aforementioned.

Entanglement-Assisted Absorption Spectroscopy

Spectroscopy is an important tool for probing the properties of materials, chemicals, and biological samples. We design a practical transmitter-receiver system that exploits entanglement to achieve a provable quantum advantage over all spectroscopic schemes based on classical sources. To probe the absorption spectra, modeled as a pattern of transmissivities among different frequency modes, we employ broadband signal-idler pairs in two-mode squeezed vacuum states. At the receiver side, we apply photodetection after optical parametric amplification. Finally, we perform a maximum likelihood decision test on the measurement results, achieving an error probability orders of magnitude lower than the optimum classical systems in various examples, including "wine tasting" and "drug testing" where real molecules are considered. In detecting the presence of an absorption line, our quantum scheme achieves the optimum performance allowed by quantum mechanics. The quantum advantage in our system is robust against noise and loss, which makes near-term experimental demonstration possible.

Rapid and nondestructive fraud detection of palm oil adulteration with Sudan dyes using portable NIR spectroscopic techniques

Non-destructive, simple and fast techniques for identifying authentic palm oil and those adulterated with Sudan dyes using portable NIR spectroscopy would be very beneficial to West Africa countries and the world at large. In this study, a portable NIR spectroscopy coupled with multivariate models were developed for detecting palm oil adulteration. A total of 520 samples of palm oil were used comprising; 40 authentic samples together with 480 adulterated samples containing Sudan dyes (I, II, III, IV of 120 samples each). Multiplicative scatter correction (MSC) preprocessing technique plus Principal component analysis (PCA) was used to extract relevant spectral information which gave visible cluster trends for authentic samples and adulterated ones. The performance of Linear discriminant analysis (LDA) and Support vector machine (SVM) were compared, and SVM showed superiority over LDA. The optimised results by cross-validation revealed that MSC-PCA + SVM gave an identification rate above 95% for both calibration and prediction sets. The overall results show that portable NIR spectroscopy together with MSC-PCA + SVM model could be used successfully to identify authentic palm oils from adulterated ones. This would be useful for quality control officers and consumers to manage and control Sudan dyes adulteration in red palm oil.

Prediction Models to Control Aging Time in Red Wine

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy

BACKGROUND

The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds.

RESULTS

Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables.

CONCLUSIONS

The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry.

Support vector machine-based open crop model (SBOCM): Case of rice production in China

Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

Manufacturer identification and storage time determination of "Dong'e Ejiao" using near infrared spectroscopy and chemometrics

We have developed a set of chemometric methods to address two critical issues in quality control of a precious traditional Chinese medicine (TCM), Dong'e Ejiao (DEEJ). Based on near infrared (NIR) spectra of multiple samples, the genuine manufacturer of DEEJ, e.g. Dong'e Ejiao Co., Ltd., was accurately identified among 21 suppliers by the fingerprint method using Hotelling T(2), distance to Model X (DModX), and similarity match value (SMV) as discriminate criteria. Soft independent modeling of the class analogy algorithm led to a misjudgment ratio of 6.2%, suggesting that the fingerprint method is more suitable for manufacturer identification. For another important feature related to clinical efficacy of DEEJ, storage time, the partial least squares-discriminant analysis (PLS-DA) method was applied with a satisfactory misjudgment ratio (15.6%) and individual prediction error around 1 year. Our results demonstrate that NIR spectra comprehensively reflect the essential quality information of DEEJ, and with the aid of proper chemometric algorithms, it is able to identify genuine manufacturer and determine accurate storage time. The overall results indicate the promising potential of NIR spectroscopy as an effective quality control tool for DEEJ and other precious TCM products.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine.

A novel near-infrared spectroscopy and chemometrics method for rapid analysis of several chemical components and antioxidant activity of mint (Mentha haplocalyx Briq.) samples

A novel near-infrared spectroscopy (NIRS) method has been researched and developed for the simultaneous analyses of the chemical components and associated properties of mint (Mentha haplocalyx Briq.) tea samples. The common analytes were: total polysaccharide content, total flavonoid content, total phenolic content, and total antioxidant activity. To resolve the NIRS data matrix for such analyses, least squares support vector machines was found to be the best chemometrics method for prediction, although it was closely followed by the radial basis function/partial least squares model. Interestingly, the commonly used partial least squares was unsatisfactory in this case. Additionally, principal component analysis and hierarchical cluster analysis were able to distinguish the mint samples according to their four geographical provinces of origin, and this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors, linear discriminant analysis, and partial least squares discriminant analysis. In general, given the potential savings with sampling and analysis time as well as with the costs of special analytical reagents required for the standard individual methods, NIRS offered a very attractive alternative for the simultaneous analysis of mint samples.

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.