Voltammetric classification of ciders with PLS-DA

Comparison of the diagnostic effectiveness of ultrasound imaging coupled with three mathematical models for discriminating thyroid nodules

BACKGROUND

The overlapping nature of thyroid lesions visualized on ultrasound (US) images could result in misdiagnosis and missed diagnoses in clinical practice.

PURPOSE

To compare the diagnostic effectiveness of US coupled with three mathematical models, namely logistic regression (Logistics), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM), in discriminating between malignant and benign thyroid nodules.

MATERIAL AND METHODS

A total of 588 thyroid nodules (287 benign and 301 malignant) were collected, among which 80% were utilized for constructing the mathematical models and the remaining 20% were used for internal validation. In addition, an external validation cohort comprising 160 nodules (80 benign and 80 malignant) was employed to validate the accuracy of these mathematical models.

RESULTS

Our study demonstrated that all three models exhibited effective predictive capabilities for distinguishing between benign and malignant nodules, whose diagnostic effectiveness surpassed that of the TI-RADS classification, particularly in terms of true negative diagnoses. SVM achieved a higher diagnostic rate for malignant thyroid nodules (93.8%) compared to Logistics (91.5%) and PLS-DA (91.6%). PLS-DA exhibited higher diagnostic rates for benign thyroid nodules (91.9%) compared to Logistics (86.7%) and SVM (88.7%). Both the area under the receiver operating characteristic curve (AUC) values of PLS-DA (0.917) and SVM (0.913) were higher than that of Logistics (0.891).

CONCLUSION

Our findings indicate that SVM had significantly higher rates of true positive diagnoses and PLS-DA exhibited significantly higher rates of true negative diagnoses. All three models outperformed the TI-RADS classification in discriminating between malignant and benign thyroid nodules.

Electrochemistry at Krakowian research institutions

The electrochemistry research team activity from Poland is marked by significant increase in the last 20 years. The joining of European Community in 2004 gives an impulse for the development of Polish science. The development of electrochemistry has been stimulated by cooperation with industry and the establishment of technology transfer centers, technology parks, business incubators, etc. and the mostly by simplified international collaborations. Five research institutions from Krakow reports work in the field of electrochemistry. The achievements of all teams are briefly described.

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

Monitoring Freeze-Damage in Grapefruit by Electric Bioimpedance Spectroscopy and Electric Equivalent Models

Grapefruit is a cold-sensitive citrus fruit, and freezing can spoil the harvest when the fruit is still on the tree and even later during manufacturing and transport due to inappropriate postharvest management. This study performed a specific Electric Impedance Spectroscopy (EIS) analysis and statistical data treatment to obtain an EIS and Artificial Neural Networks (ANN)-based model for early freeze-damage detection in grapefruit showing a Correct Correlation Rate of 100%. Additionally, Cryo-Field Emission Scanning Electron Microscopy observations were conducted on both fresh and frozen/thawed samples, analyzing the different impedance responses in order to understand the biological changes in the tissue. Finally, a modified Hayden electric equivalent model was parameterized to simulate the impedance response electrically and link the electric behavior of biological tissue to the change in its properties due to freezing. The developed technique is introduced as an alternative to the traditional ones, as it is fast, economic, and easy to carry out.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

A novel analytical strategy for discriminating antibiotic mycelial residue adulteration in feed based on ATR-IR and microscopic infrared imaging

The Antibiotic mycelial residue (AMR) contains antibiotic residue, there are safety risks if it is used illegally in feed. This study investigated the feasibility of qualitative identification of AMR in protein feed and self-prepared feed based on attenuated total reflection mid-infrared spectrum (ATR-IR) and microscopic infrared imaging. Cottonseed meal (CM), soybean meal (SM), distillers dried grains with solubles (DDGS), nucleotide residue (NR), oxytetracycline residue (OR) and streptomycin sulfate residue (SR) and two self-prepared feed (broiler and pig) were used as research objects. The results showed that there were characteristic peaks at 1614 cm^-1, 1315 cm^-1, 779 cm^-1, 514 cm^-1 in the ATR-IR spectra of AMR, which were related to calcium oxalate hydrate. After detection, the content of total calcium and calcium oxalate in AMR were higher than those in protein feed. ATR-IR can quickly realize the qualitative discrimination of pure material samples. The combination of ATR-IR and partial least squares discriminant analysis (PLSDA) was effective in discriminating AMR from CM and SM with a single component (the classification errors were 0), but it cannot meet the discrimination of AMR from the fermented protein feed (such as DDGS and NR, the classification errors were 0.10 and 0.12) and self-prepared feed with complex components. Compared with ATR-IR, microscopic infrared imaging was less affected by the sample complexity. Multi-component samples belong to physical mixing and will not affect the infrared spectra of each component. Therefore, microscopic infrared imaging combined with effective information extraction algorithms such as cosine similarity can distinguish OR in the fermented protein feed and self-prepared feed. The above results showed that the advantages of ATR-IR and microscopic infrared imaging were complementary, which provided a new idea for the discrimination analysis of illegal feed additives.

Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus

The taste of fresh mushrooms is always appealing. Phlebopus portentosus is the only porcini that can be cultivated artificially in the world, with a daily output of up to 2 tons and a large sales market. Fresh mushrooms are very susceptible to microbial attacks when stored at 0-2 °C for more than 5 days. Therefore, the freshness of P. portentosus must be evaluated during its refrigeration to ensure food safety. According to their freshness, the samples were divided into three categories, namely, category I (1-2 days, 0-48 h, recommended for consumption), category II (3-4 days, 48-96 h, recommended for consumption), and category III (5-6 days, 96-144 h, not recommended). In our study, a fast and reliable shelf life identification method was established through Fourier transform near-infrared (FT-NIR) spectroscopy combined with a machine learning method. Deep learning (DL) is a new focus in the field of food research, so we established a deep learning classification model, traditional support-vector machine (SVM), partial least-squares discriminant analysis (PLS-DA), and an extreme learning machine (ELM) model to identify the shelf life of P. portentosus. The results showed that FT-NIR two-dimensional correlation spectroscopy (2DCOS) combined with the deep learning model was more suitable for the identification of fresh mushroom shelf life and the model had the best robustness. In conclusion, FT-NIR combined with machine learning had the advantages of being nondestructive, fast, and highly accurate in identifying the shelf life of P. portentosus. This method may become a promising rapid analysis tool, which can quickly identify the shelf life of fresh edible mushrooms.

Classification of polymorphic forms of fluconazole in pharmaceuticals by FT-IR and FT-NIR spectroscopy

The main goal of this work was to test the ability of vibrational spectroscopy techniques to differentiate between different polymorphic forms of fluconazole in pharmaceutical products. These are mostly manufactured with fluconazole as polymorphic form II and form III. These crystalline forms may undergo polymorphic transition during the manufacturing process or storage conditions. Therefore, it is important to have a method to monitor these changes to ensure the stability and efficacy of the drug. Each of FT-IR or FT-NIR spectra were associated to partial least squares-discriminant analysis (PLS-DA) for building classification models to distinguish between form II, form III and monohydrate form. The results has shown that combining either FT-IR or FT-NIR to PLS-DA has a high efficiency to classify various fluconazole polymorphs, with a high sensitivity and specificity. Finally, the selectivity of the PLS-DA models was tested by analyzing separately each of three following samples by FT-IR and FT-NIR: lactose monohydrate, which is an excipient mostly used for manufacturing fluconazole pharmaceutical products, itraconazole and miconazole. These two last compounds mimic potential contaminants and belong to the same class as fluconazole. Based on the plots of Hotelling's T² vs Q residuals, pure compounds of miconazole and itraconazole, that were analyzed separately, were significantly considered outliers and rejected. Furthermore, binary mixtures consist of fluconazole form-II and monohydrate form with different ratios were used to test the suitability of each technique FT-IR and FT-NIR with PLS-DA to detect minimum contaminant or polymorphic conversion from a polymorphic form to another using also the plots of Hotelling's T² vs Q residuals.

Determination of milk fat authenticity in ultra-filtered white cheese by using Raman spectroscopy with multivariate data analysis

Cheese is one of the most widely consumed food products in the world. However, the increasing demand for nutritionally enhanced or functional products by the cheese industry has created new approaches that partially or fully replace milk fat. With this, new methods of adulteration have also been noted, potentially leading to these fully/partially-replaced products being offered as cheese. In this study, Raman spectroscopy was used to determine origins of fats in margarine, corn, and palm oils present in white and ultra-filtered cheese samples. Raman spectra were evaluated with partial least square-discriminant (PLS-DA) and PLS to identify fat/oil origins and adulteration ratios. The coefficients of determination and limits of detection for margarine, and corn and palm oil adulteration were found to be 0.990, 0.993, 0.991 and 3.38%, 3.36% and 3.59%, respectively.

Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy

Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%).

Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.

Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy

The early detection of freeze damage in Navelate oranges (Citrus sinensis L. Osbeck) was studied using electrochemical impedance spectroscopy (EIS), which is associated with a specific double-needle sensor. The objective was to identify this problem early in order to help to determine when a freeze phenomenon occurs. Thus, we selected a set of Navelate oranges without external defects, belonging to the same batch. Next, an intense cold process was simulated to analyze the oranges before and after freezing. The results of the spectroscopy analysis revealed different signals for oranges depending on whether they had experienced freezing or not. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) of the obtained data demonstrated that it is possible to discriminate the samples, explaining 88.5% of the total variability (PCA) and being able to design a mathematical model with a prediction sensitivity of 80% (PLS-DA). Additionally, a designed artificial neural network (ANN) prediction model managed to correctly classify 100% of the studied samples. Therefore, EIS together with ANN-based data treatment is proposed as a viable alternative to the traditional techniques for the early detection of freeze damage in oranges.

Differentiation and comparison of Wolfiporia cocos raw materials based on multi-spectral information fusion and chemometric methods

In order to achieve the target of deeper insight into the differentiation and comparison of Wolfiporia cocos, a total of 350 samples including distinct growth patterns, various collection regions and different medicinal parts were investigated using multi-spectral information fusion based on ultraviolet (UV) and Fourier transform infrared (FT-IR) spectroscopies coupled with chemometrics. From the results, the discrimination of samples was obtained successfully and good classification performances were shown according to partial least squares discriminant analysis (PLS-DA) models. Comparatively, the distinctness of chemical information in the two medicinal parts of W. cocos were much more than that in the same part with different growth patterns and collection areas. Meanwhile, an interesting finding suggested that growth patterns rather than geographical origins could be the dominant factor to effect the chemical properties of the same part samples, especially for the epidermis. Compared with the epidermis samples, there were better quality consistency for the inner part of W. cocos. Totally, this study demonstrated that the developed method proved to be reliable to perform comparative analysis of W. cocos. Moreover, it could provide more comprehensive chemical evidence for the critical supplement of quality assessment on the raw materials of W. cocos.

Fingerprinting Green Curry: An Electrochemical Approach to Food Quality Control

The detection and identification of multiple components in a complex sample such as food in a cost-effective way is an ongoing challenge. The development of on-site and rapid detection methods to ensure food quality and composition is of significant interest to the food industry. Here we report that an electrochemical method can be used with an unmodified glassy carbon electrode for the identification of the key ingredients found within Thai green curries. It was found that green curry presents a fingerprint electrochemical response that contains four distinct peaks when differential pulse voltammetry is performed. The reproducibility of the sensor is excellent as no surface modification is required and therefore storage is not an issue. By employing particle swarm optimization algorithms the identification of ingredients within a green curry could be obtained. In addition, the quality and freshness of the sample could be monitored by detecting a change in the intensity of the peaks in the fingerprint response.

Quality Assessment of Gentiana rigescens from Different Geographical Origins Using FT-IR Spectroscopy Combined with HPLC

Gentiana rigescens is a precious herbal medicine in China because of its liver-protective and choleretic effects. A method for the qualitative identification and quantitative evaluation of G. rigescens from Yunnan Province, China, has been developed employing Fourier transform infrared (FT-IR) spectroscopy and high performance liquid chromatography (HPLC) with the aid of chemometrics such as partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) regression. Our results indicated that PLS-DA model could efficiently discriminate G. rigescens from different geographical origins. It was found that the samples which could not be determined accurately were in the margin or outside of the 95% confidence ellipses. Moreover, the result implied that geographical origins variation of root samples were more obvious than that of stems and leaves. The quantitative analysis was based on gentiopicroside content which was the main active constituent in G. rigescens. For the prediction of gentiopicroside, the performances of model based on the parameters selected through grid search algorithm (GS) with seven-fold cross validation were better than those based on genetic algorithm (GA) and particle swarm optimization algorithm (PSO). For the SVM-GS model, the result was satisfactory. FT-IR spectroscopy coupled with PLS-DA and SVM-GS can be an alternative strategy for qualitative identification and quantitative evaluation of G. rigescens.

Discrimination of Apple Liqueurs (Nalewka) Using a Voltammetric Electronic Tongue, UV-Vis and Raman Spectroscopy

The capability of a phthalocyanine-based voltammetric electronic tongue to analyze strong alcoholic beverages has been evaluated and compared with the performance of spectroscopic techniques coupled to chemometrics. Nalewka Polish liqueurs prepared from five apple varieties have been used as a model of strong liqueurs. Principal Component Analysis has demonstrated that the best discrimination between liqueurs prepared from different apple varieties is achieved using the e-tongue and UV-Vis spectroscopy. Raman spectra coupled to chemometrics have not been efficient in discriminating liqueurs. The calculated Euclidean distances and the k-Nearest Neighbors algorithm (kNN) confirmed these results. The main advantage of the e-tongue is that, using PLS-1, good correlations have been found simultaneously with the phenolic content measured by the Folin-Ciocalteu method (R² of 0.97 in calibration and R² of 0.93 in validation) and also with the density, a marker of the alcoholic content method (R² of 0.93 in calibration and R² of 0.88 in validation). UV-Vis coupled with chemometrics has shown good correlations only with the phenolic content (R² of 0.99 in calibration and R² of 0.99 in validation) but correlations with the alcoholic content were low. Raman coupled with chemometrics has shown good correlations only with density (R² of 0.96 in calibration and R² of 0.85 in validation). In summary, from the three holistic methods evaluated to analyze strong alcoholic liqueurs, the voltammetric electronic tongue using phthalocyanines as sensing elements is superior to Raman or UV-Vis techniques because it shows an excellent discrimination capability and remarkable correlations with both antioxidant capacity and alcoholic content-the most important parameters to be measured in this type of liqueurs.