somaye vali zade

Ensuring meat quality is critical for consumer satisfaction and food safety, but monitoring subtle quality changes remains a challenge. This study presents a smartphone-based imaging approach combined with advanced data analysis techniques to evaluate color changes in red meat under different storage conditions. Meat samples stored in a refrigerator (4°C) and a freezer (-19°C) were analyzed over three weeks using RGB and HSV color spaces. Principal Component Analysis (PCA) revealed patterns of color change, while ANOVA-Simultaneous Component Analysis (ASCA) identified significant effects of storage time and temperature on meat color, with the HSV color space showing greater sensitivity. Partial Least Squares Discriminant Analysis (PLS-DA) successfully classified chilled and frozen samples after temperature equilibration, with the soft classification method demonstrating robust performance. These results highlight the potential of integrating accessible imaging tools and machine learning techniques for objective and efficient meat quality assessment, providing a scalable solution for the food industry.

Standard normal variate (SNV) preprocessing is widely applied to spectroscopic data prior to multivariate modeling, under the assumption that it mitigates undesired background variations while preserving analyte signal information. However, the scaling step in SNV, where each spectrum is divided by its standard deviation, could potentially distort the covariance between spectral intensities and component concentrations. This study systematically evaluates the effect of SNV preprocessing on the ability to develop accurate quantitative models for determining analyte concentrations in mixtures, with a focus on the determination of moisture in corn using NIR spectroscopy.

Simulations were performed to generate single and multi-component spectroscopic datasets with varying levels of multiplicative scatter and baseline offset effects. Additionally, an experimental near-infrared (NIR) dataset for corn samples with reference moisture values was utilized. Partial least squares (PLS) regression was employed to model the simulated and experimental data, with and without SNV preprocessing. Model calibration and prediction performance metrics were assessed.

For simulated datasets without background interferences, SNV preprocessing eliminated useful concentration-related variations by forcing all sample spectra to equal lengths, severely degrading PLS calibration and prediction abilities. In scenarios with multiplicative/additive perturbations, while SNV mean-centering helped mitigate these undesired effects, the subsequent scaling step obscured analyte concentration information in the spectral intensities. PLS models built from raw corn NIR spectra provided excellent determination of moisture in corn using NIR spectroscopy, whereas SNV preprocessing led to significantly higher prediction errors. The findings demonstrate that indiscriminate application of SNV can be detrimental for precise quantitative spectroscopic analysis by disrupting the covariance between signals and analyte levels. Therefore, preprocessing strategies should be judiciously evaluated based on the specific data characteristics and modeling objectives.

This study introduces parallel coordinates plots as an innovative visualization method, offering an alternative to traditional score plots in Principal Component Analysis (PCA) for exploratory data analysis (EDA). EDA is pivotal for unraveling intricate data structures, and PCA serves as a powerful tool for simplifying complex datasets. The paper explores the application of parallel coordinates plots in visualizing and interpreting PCA results, emphasizing their effectiveness in revealing multivariate patterns and detecting outliers. Through the examination of simulated datasets, where two distinct classes are generated in three-dimensional and five-dimensional spaces, and real Raman spectroscopic data, the research demonstrates the utility of parallel coordinates plots in enhancing data exploration and providing valuable insights in pattern recognition methodologies. The results highlight the complementary nature of traditional score plots and parallel coordinates plots, offering a comprehensive approach to understanding high-dimensional datasets. This innovative visualization technique proves valuable in discerning subtle patterns and relationships within complex data structures, contributing to a more profound exploration of datasets in various domains.

In the realm of analytical chemistry, multivariate calibration involves creating mathematical models that connect diverse instrumental signals with analyte concentrations. This approach provides a mean to quantitatively analyze complex mixtures, particularly in multicomponent systems. To address food adulteration concerns, this paper explores the application of Raman spectroscopy and Partial Least Squares Regression (PLSR) using the MVC1 software. The main objective is to demonstrate the software's efficiency in quantifying the adulteration of hazelnut oil in extra virgin olive oil (EVOO). The analysis leverages the MVC1 software, a valuable tool for multivariate linear and nonlinear calibrations. One-leave-out cross-validation and the Durbin-Watson statistical test are employed to determine the optimal number of PLS factors and identify outliers. Statistical parameters including RMSEP, %REP, R², and explained variance are used to evaluate the calibration model's performance. Key figures of merit including sensitivity, analytical sensitivity, LOD, and LOQ, are computed to assess the analytical technique's precision and reliability.Results and The study effectively quantifies the percentage of adulteration in EVOO by hazelnut oil, a pressing concern in food authenticity and safety. The results demonstrate the MVC1 software's capability in establishing reliable calibration models. By achieving a balance between sensitivity and analytical sensitivity, the model accurately predicts analyte concentrations. It also sets robust detection and quantitation limits, ensuring precise analysis. This research showcases the practical application of advanced analytical techniques and software tools to address real-world problems, contributing to the authenticity and purity of food products in the market.

 Biopharmaceutical products, characterized by strategic importance and substantial added value, have garnered significant attention from global governments and investors. In Iran's scientific roadmap, the ambitious goal of attaining a three percent share in the global market by the year 2025 has been established, necessitating the formulation and implementation of targeted strategies.

 This research employs a comprehensive mixed-methods approach, encompassing both quantitative and qualitative components. Quantitatively, official annual statistics from the Food and Drug Administration are analyzed to evaluate the growth trend of domestic biopharmaceutical production and imports. The qualitative phase involves in-depth semi-structured interviews with 25 industry experts.

 Analysis reveals a gradual progression towards the targeted horizon, with a noteworthy 68.3% growth compared to the global market, representing 0.5% of the 3% target for the year 2025.

 Despite this, challenges persist, impeding the realization of the established objective. Overcoming these obstacles and implementing the proposed solutions are imperative for expediting progress. The study anticipates Iran surmounting these barriers and securing a 3% share in the global biopharmaceutical market in the foreseeable future.

Understanding how polyprotic acids behave is critical in various fields, including chemistry, biology, and medicine. Elucidation of the behavior of polyprotic acids through utilization of alpha functions, which provide insightful graphical representations of species distribution with varying pH levels, has been considered. The current paper offers an in-depth exploration of alpha functions and their significance in portraying the proportions of ionized and non-ionized species, a crucial aspect in predicting acid behavior, buffering capacity, and identifying optimal pH conditions.

The Microsoft Excel and its Solver tool to explore polyprotic acid equilibria have been utilized. Synthetic datasets were generated using Excel spreadsheets, while Solver facilitated non-linear curve fitting for parameter optimization.

A novel contribution of this paper lies in introducing Excel-based tools that facilitate the generation of alpha function plots. These tools simplify the process of visualizing species distribution and pave the way for more advanced analyses. Additionally, the paper showcases the utilization of Excel Solver for robust nonlinear curve fitting of dissociation constants. This advanced computational technique enhances the accuracy of dissociation constant determination, ensuring more reliable predictions. An intriguing aspect of the proposed method is its ability to exhibit robustness even when the total acid concentration is unknown. This characteristic sets the approach apart, overcoming a common challenge in acid-base studies and analytical chemistry. The ability to accurately determine dissociation constants in such circumstances further underscores the practicality and relevance of the method. The implications of this research extend beyond the realm of theoretical understanding. The practical applications of the findings are particularly noteworthy, with a significant impact on pharmaceutical industries. The insights gained from alpha function analysis can be instrumental in drug formulation processes and in optimizing the solubility of pharmaceutical compounds. These applications can potentially enhance drug efficacy, patient experience, and overall healthcare outcomes.

The Nobel Prize in Chemistry 2023 has been awarded to Moungi G. Bawendi, Louis E. Brus, and Alexei I. Ekimov for their pioneering work on quantum dots. Quantum dots are nanocrystals just a few nanometers in size that exhibit unique optical and electronic properties due to quantum confinement effects. In the early 1980s, Ekimov and Brus independently discovered that the optical absorption of semiconductor nanoparticles shifts to blue wavelengths as the particles shrink in size below 10 nm. This demonstrated that quantum confinement alters the fundamental properties of materials at the nanoscale. In 1993, Bawendi revolutionized quantum dot production by developing a simple, scalable method to synthesize high-quality dots with precisely controlled sizes. Thanks to these advances, quantum dots are now used in diverse applications including QLED displays, LED lighting, biomedical imaging, and catalysis. The laureates’ foundational work opened up the new field of nanoscience and enabled the real-world exploitation of quantum phenomena, underscoring how quantum effects can provide powerful new tools when materials are engineered at the nanoscale. Their discoveries established quantum dots as an important class of nanoparticles with properties that can be precisely tuned through size control.

Basic understanding of Raman spectroscopy by strengthening the attitude towards this powerful method can help to expand its applications. Combination of chemometrics multivariate analysis methods with other instrumental analysis is useful to find proper solutions for many research and practical problems. In this study, analysis of Raman spectroscopy data by chemometrics methods has been reviewed. The developed approach based on chemometrics methods is a valid, accurate, and cost-effective route in authentication of halal foods.

Supervised pattern recognition methods are the multivariate chemometrics techniques that are used for qualitative identification, authentication and discriminant analysis in different fields, including food researches. The principal component analysis method provides the possibility of visualizing and exploring in the data space by reducing the dimensions of the data and is an introduction to the classification methods. Authentication of bovine skin and bone gelatin is possible with Raman spectroscopy measurements and the use of supervised pattern recognition methods. The simple and non-invasive measurement of Raman spectra and the application of multivariate chemometrics methods for their analysis leads to the creation of solutions for many problems in the field of qualitative identification and quantitative analysis in complex systems.