Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

Identification and quantification of goat milk adulteration using mid-infrared spectroscopy and chemometrics.

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram.

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Social environment, health cognition, and health behavior: how individuals with non-fixed employment end up with adverse health outcomes in China under the era of VUCA?-findings from PLS-SEM and fsQCA.

Objectives: This article studied the single-factor causal relationships between the social environment, health cognition, and health behavior of the individuals with non-fixed employment and their adverse health outcomes, as well as the complex causal relationships of multiple factors on these outcomes. Methods: Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) are employed. Data is collected from the results of an open questionnaire Psychology and Behavior Investigation of Chinese Residents 2021. Results: PLS-SEM analysis reveals that health risk behaviors and cognition play a mediating role in impact of the social environment on adverse health outcomes, indicating that individuals with non-fixed employment susceptible to adverse health outcomes. fsQCA analysis identifies that weak social support is a core condition leading to outcomes of depression and anxiety. There are shared configurations and causal pathways between the outcomes of physical health and depression. Conclusion: The study supports the social determinants theory of health and suggests that the fundamental reason for people being trapped in adverse health outcomes is the health inequality caused by social stratification, and the external shock of uncertainty in the era of VUCA (Volatility, Uncertainty, Complexity, and Ambiguity).

Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.

OBJECTIVES: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. MATERIALS AND METHODS: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. RESULTS: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. CONCLUSIONS: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

Determination of leaf nitrogen content in apple and jujube by near-infrared spectroscopy.

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R2), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients.

Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction.

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut's quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

Evaluation of Saffron Quality Using Rapid Quantitative Inspection Technology with Near-Infrared Spectroscopy.

A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky-Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R2 values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system.

Characteristics of Indonesian Stingless Bee Propolis and Study of Metabolomic Properties Based on Region and Species.

The chemical compounds found in propolis vary according to plant sources, species, and geographical regions. To date, Indonesian propolis has not yet become standardized in terms of its chemical constituents. Thus, this study aimed to identify the presence of marker compounds and determine whether different classes of Indonesian propolis exist. In this study, yields, total polyphenol content (TPC), total flavonoid content (TFC), and antioxidants were measured. Identification of chemical compounds was carried out with Fourier-transform infrared (FTIR) spectroscopy and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Metaboanalyst 6.0 was employed in conducting principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using the results of the FTIR and LC-MS/MS. The propolis with the highest TFC, TPC, and antioxidant activity was Geniotrigona thoracica from North Sumatra. The results of propolis compound mapping based on region with discriminant analysis revealed that types of propolis from Java have similar characteristics. Then, based on species, the types of propolis from Tetragonula laeviceps and Heterotrigona itama have special characteristics; the samples from these species can be grouped according to similar characteristics. In conclusion, 10 potential marker compounds were identified in Indonesian propolis, enabling regional and species-specific varieties of Indonesian propolis to be classified based on chemical composition mapping.

[Prediction model of Reduning Injection content based on near-infrared and mid-infrared technology combined with spectral fusion].

In this paper, a method for rapidly determining the content of chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, gardeniside, and strychnoside in Reduning Injection(RI) was established based on near-infrared spectroscopy(NIRS), midinfrared spectroscopy(MIRS), and spectral fusion technology. Six pretreatment methods and five variable screening methods were investigated, and the best method was selected to establish a partial least square(PLS) model of two single spectra. At the same time,the NIRS and MIRS were fused with equal weights and characteristic bands, and the PLS model was established. The prediction effect of the four models on the quality control components was compared: NIRS>characteristic band fusion>MIRS>equal weight fusion. The relative standard error of prediction(RSEP) of the NIRS models on the five quality control components was less than 2. 5%, and the ratio of performance to deviation(RPD) was greater than 9. 5. The results show that the single spectrum model of NIRS is the best quantitative detection method, and the model of NIRS combined with the PLS algorithm can be used for the rapid detection of Reduning Injection.

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis.

This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the data using least-squares regression and evaluating the model fit using the coefficient of determination (R-squared). The results highlight the strong performance of the quadratic polynomial fit, as evidenced by high R-squared values, indicating the model's ability to explain a substantial proportion of the data variability. Comparisons with linear and cubic models further underscore the quadratic model's balance between simplicity and precision for many practical applications. The study also acknowledges the limitations of quadratic polynomials and proposes future research directions to enhance their accuracy and efficiency for diverse data analysis tasks. This research bridges the gap between theoretical concepts and practical implementation, providing an accessible Python-based tool for leveraging quadratic polynomials in data analysis.

This study examines how quadratic polynomials, which are mathematical equations used to model and understand patterns in data, can be effectively applied using Python, a versatile programming language with libraries suited for mathematical and visual analysis. Researchers have focused on the adaptability of these polynomials in various fields, from software analytics to materials science, in order to provide practical Python code examples. They also discussed the predictive accuracy of the method, confirmed through a statistical measure called R-squared, and acknowledged the need for future research to integrate more complex models for richer data interpretation.

Pilot Study on the Use of Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy for Diagnosing and Characterizing Cardiac Amyloidosis.

Amyloidosis diagnosis relies on Congo red staining with immunohistochemistry and immunofluorescence for subtyping but lacks sensitivity and specificity. Laser-microdissection mass spectroscopy offers better accuracy but is complex and requires extensive sample preparation. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy offers a promising alternative for amyloidosis characterization. Cardiac tissue sections from nine patients with amyloidosis and 20 heart transplant recipients were analyzed using ATR-FTIR spectroscopy. Partial least squares discriminant analysis (PLS-DA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) models were used to differentiate healthy post-transplant cardiac tissue from amyloidosis samples and identify amyloidosis subtypes [κ light chain (n = 1), λ light chain (n = 3), and transthyretin (n = 5)]. Leave-one-out cross-validation (LOOCV) was employed to assess the performance of the PLS-DA model. Significant spectral differences were found in the 1700-1500 cm-1 and 1300-1200 cm-1 regions, primarily related to proteins. The PLS-DA model explained 85.8% of the variance, showing clear clustering between groups. PCA in the 1712-1711 cm-1, 1666-1646 cm-1, and 1385-1383 cm-1 regions also identified two clear clusters. The PCA and the HCA model in the 1646-1642 cm-1 region distinguished κ light chain, λ light chain, and transthyretin cases. This pilot study suggests ATR-FTIR spectroscopy as a novel, non-destructive, rapid, and inexpensive tool for diagnosing and subtyping amyloidosis. This study was limited by a small dataset and variability in measurements across different instruments and laboratories. The PLS-DA model's performance may suffer from overfitting and class imbalance. Larger, more diverse datasets are needed for validation.

[Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter].

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

Multiobjective optimization of effect-directed planar chromatography as a promising tool for fast selection of polypotent natural products.

A new method for efficiently selecting polypotent natural products is proposed in this study. The method involves using effect-directed HPTLC data and multiobjective optimization algorithms to extract chromatographic signals from HPTLC bioassay images. Three different multiobjective optimization methods, namely Derringer's desirability approach, Technique for order of preference by similarity to ideal solution (TOPSIS), and Sum of ranking differences (SRD), were applied to the chromatographic signals. In combination with jackknife cross-validation, Derringer's approach and TOPSIS demonstrated high similarity in finding the best (most polypotent), next to the best, next to the worst, and worst (least polypotent) extracts, while the SRD resulted in slightly different outcomes. Furthermore, a new method for identifying the chromatographic features that characterize the most polypotent extracts was proposed. This method is based on partial least square regression (PLS) and can be used in combination with HPTLC-chemical fingerprints to predict the desirability of new extracts. The resulting PLS models demonstrated high statistical performance with determination coefficients ranging from R2 = 0.885 in the case of Derringer's desirability, to 0.986 for TOPSIS. However, the PLS modeling of SRD values was not successful.

Rapid prediction of the chemical composition of pet food using a benchtop and handheld near-infrared spectrometer.

Quality of pet foods can be affected by many factors such as raw materials, formulations, and processing techniques. The pet food manufacturers require fast analyses to control the nutritional quality of their products. Herein, near-infrared spectroscopy (NIR) was evaluated to quantify the chemical composition of pet food, and the performances of two NIR spectrometers were investigated and compared: a benchtop instrument (1000-2500 nm) and a low-cost handheld instrument (900-1700 nm). Seventy cat food and thirty-six dog samples were characterized using reference methods for crude protein, crude fat, crude fibre, crude ash, moisture, calcium (Ca), and phosphorus (P). Principal component regression (PCR) and partial least squares regression (PLSR) were used to establish the models that involved the cat food and mixed model. The characteristic wavelengths were selected using a competitive adaptive reweighted-sampling (CARS) algorithm. The Optimal models obtained from the benchtop instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (Residual prediction variation (RPD) > 3), for crude fibre were classified as "Poor" (RPD>2), and for crude ash, Ca and P (RPD<2) were classified as "Very poor". The Optimal calibrations obtained from the handheld instrument for crude protein, crude fat, and moisture were classified as "Good" or "Very good" (RPD>3), for crude fibre, crude ash, Ca, and P were classified as "Very poor" (RPD<2). Generally, the the performance of benchtop and handheld instrument was close, and the cat food model outperformed the mixed model. Results from the current study revealed the potential to monitor the chemical compositions in pet food on a large scale.

Urine Metabolites Changes in Acute Myocardial Infarction Rats via Metabolomic Analysis.

OBJECTIVES: To screen biomarkers for forensic identification of acute myocardial infarction (AMI) by non-targeted metabolomic studies on changes of urine metabolites in rats with AMI. METHODS: The rat models of the sham surgery group, AMI group and hyperlipidemia + acute myocardial infarction (HAMI) group were established. Ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze the changes of urine metabolic spectrometry in AMI rats. Principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis were used to screen differential metabolites. The MetaboAnalyst database was used to analyze the metabolic pathway enrichment and access the predictive ability of differential metabolites. RESULTS: A total of 40 and 61 differential metabolites associated with AMI and HAMI were screened, respectively. Among them, 22 metabolites were common in both rat models. These small metabolites were mainly concentrated in the niacin and nicotinamide metabolic pathways. Within the 95% confidence interval, the area under the curve (AUC) values of receiver operator characteristic curve for N8-acetylspermidine, 3-methylhistamine, and thymine were greater than 0.95. CONCLUSIONS: N8-acetylspermidine, 3-methylhistamine, and thymine can be used as potential biomarkers for AMI diagnosis, and abnormal metabolism in niacin and nicotinamide may be the main causes of AMI. This study can provide reference for the mechanism and causes of AMI identification.

In-house validation of a visible and near infrared spectroscopy non-targeted method to support panel test of virgin olive oils.

In this study, an in-house validation of Visible and Near Infrared Spectroscopy was performed to distinguish between extra virgin olive oil (EVOO) and virgin olive oil (VOO). A total of 161 samples of olive oil of three different categories (EVOO, VOO and lampante (LOO)) were analysed by transflectance using a monochromator instrument. One-class models were initially developed using Partial Least Squares (PLS) Density Modelling to characterize EVOO and VOO category. Once the LOO samples were discriminated, linear and non-linear discriminant models were built to classify EVOO and VOO. Different data pre-treatments and variable selection algorithms were evaluated to establish the best models in terms of Correct Classification Rate (CCR). The best model, obtained after variable selection using PLS Discriminant Analysis, yielded CCR values of 82.35 % for EVOO and 66.67 % for VOO in external validation. These results confirmed that VIS + NIRS technology may be used to provide rapid, non-destructive preliminary screening of olive oil samples for categorization; suspect samples may then be analysed by official analytical methods.