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As the consumer interest and market for plant-based meat alternatives grows, understanding the nutritional differences between alternative and traditional meats is essential. This article describes an untargeted gas chromatography–mass spectrometry (GC–MS)-based metabolomics approach that compares the chemical profiles of a popular plant-based meat alternative and grassfed ground beef using a GC system coupled to a GC–MS device. The samples were derivatized to simplify the chromatographic process and render the polar metabolites more volatile for GC–MS analysis. Statistical and multivariate analysis of the acquired and processed GC–MS data revealed that 90% of the annotated compounds differed between the plant-based alternative meat and the grass-fed ground beef samples. The ground beef and plant-based products each contained several compounds that were found in much smaller quantities, or not at all, in the other product. These results indicate differences in organic composition even though the nutritional labels on the back of the products were nearly identical. Heat maps, principal component analysis (PCA) score plots, variable importance plots (VIPs), and the clustering of compounds into metabolite classes provided further insights into the differences between the two types of meat products. The biological significance of the comparative data was studied using online databases and pathway analysis tools. [ABSTRACT FROM AUTHOR]

Fingernails can accumulate drugs as a result of chronic exposure. This work employed Raman spectroscopy for detecting cocaine hydrochloride (HCl) and its impurities within fingernails, utilizing orbital raster scanning (ORS) technology, where the laser beam hits multiple positions within the sample. Doing so maintained sensitivity and ensured that more of each sample's components were represented. Fingernails were spiked with powder and solution forms of cocaine HCl and its impurities, including benzocaine HCl, levamisole HCl, lidocaine HCl, and procaine HCl. The strong Raman scattering observed for these substances indicated a high drug accumulation in the fingernails. Key cocaine HCl bands were seen at 848, 874, and 898 cm-1 (C-C stretching-tropane ring), 1004 cm-1 (symmetric stretching-aromatic ring), 1278 cm-1 (C-N stretching), 1453 cm-1 (asymmetric CH3 deformation), and 1605 and 1712 cm-1 (C=C and C=O stretching). Principal components analysis (PCA) confirmed that 90% (nails spiked with drug powders) and 77.2% (nails spiked with drug solutions) were accounted for in the variance among the data. The findings showed that Raman spectroscopy identified the presence of cocaine HCl and its impurities within fingernails. [ABSTRACT FROM AUTHOR]

The process of aspirin synthesis was studied using in situ real-time attenuated total reflection Fourier transform infrared spectroscopy (ATR-FT-IR) from salicylic acid and acetic anhydride accompanied by a sulfuric acid catalyst. The data collected from the IR analysis process showed that there were obvious changes in the position, shape, and intensity of the carbonyl characteristic peaks in the three-dimensional (3D) spectra. Principal component analysis (PCA) followed by target transformation of the resulting factors was used to analyze the spectra and the relationship of the absorbance and time trendlines of raw materials and products in the reaction system. This method would be enough to monitor reaction progress and determine the end point without further separation and purification of the reaction mixture. The results demonstrated that the acetylation of salicylic acid catalyzed by sulfuric acid was a complicated process, and the conversion rate was high. The reaction was a rapid reaction mechanism and could be divided into two stages with different reaction rate. The ATR-FT-IR spectroscopy technique can monitor and analyze the changes of raw materials and products with time, which is more reliable to determine fundamental data for process analysis and optimization using real-time monitoring of the reaction process. [ABSTRACT FROM AUTHOR]

Honey peaches can bruise during harvesting, handling, storage, transportation, and distribution. In this study, the spectral range used was 400-1100 nm, and we extracted the RGB and HSI color space characteristics of the images. After principal component analysis (PCA) of the original data, the gray histogram features of the PC1 images were extracted. Partial least squares qualitative discriminant analysis (PLS-DA) and extreme learning machine (ELM) discriminant models were established. Among the 38 color features, the PLS-DA and ELM models had a high rate of misclassification, and the best classification accuracy was 74.29%. When extracting the spectral information of the bruised sample to build the model, the highest classification accuracy was 92.86% for the 176 characteristic wavelength points of the full band. In contrast, only 40 wavelength bands were used after selecting the genetic algorithm's valid information. The classification accuracy of the PLS-DA model was 100%, which is because the softening and browning of the peach was not apparent after early bruising. However, the changes in the tissue's thermal properties caused by internal defects are expressed in the internal spectrum. Therefore, the shortwave NIR hyperspectral imaging technique's spectral information can detect the early bruising of peaches. [ABSTRACT FROM AUTHOR]

In this paper, we show surface-enhanced Raman spectroscopy (SERS) of serum from acute leukemia and analyze the SERS through the multivariate statistical method of principal component analysis (PCA). First, the changes in the structure of proteins, lipids, carbohydrates, and other substances in the serum of patients with acute leukemia are analyzed from the molecular perspective. The results showed that the ordered structure of the protein main chain and the side chain conformation was weakened, the main chain structure was almost broken, and the protein became loose and disordered. In addition, the content of esters, amino acids, glycoproteins, and carbohydrate-related substances decreases, indicating a change in the environment. Finally, the PCA method was used to analyze the SERS signal of the serum of healthy people and patients with acute leukemia to accurately distinguish the two serum samples. The above conclusions can provide a favorable experimental basis for diagnosing leukemia and the study of the biochemical mechanism involved. [ABSTRACT FROM AUTHOR]

This article describes a compendium of chemometrics applications in separation science to demonstrate the importance and synergy of data handling. We review the main points that must be raised when using multivariate techniques and list references for further reading. With examples in food science, the case studies comprise of applications of pattern recognition (principal component analysis, PCA), regression (partial least squares, PLS), and classification methods (soft independent modelling of class analogies, SIMCA). [ABSTRACT FROM AUTHOR]

In this paper, a portable near-infrared (NIR) spectrometer, combined with chemometrics methods, was used for rapid identification of 20 wood species. Four spectral pretreatment methods, including Norris-Williams smooth (NWS), standard normal variate (SNV), multiplicative scatter correction (MSC) and Savitzky-Golay 1st derivative (SG 1st-Der), were adopted for noise reduction and information enhancement of near-infrared (NIR) spectra of wood. Three pattern recognition methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM), were used to cluster analysis of sample spectra. The competitive adaptive reweighted sampling (CARS) method was proposed to select effective wavelengths (EWs). The Bayesian information criteria (BIC) value was used for comprehensive evaluation of model accuracy and complexity. Compared with PLS-DA models, both the correction set and test set of the SG 1st-Der-SVM model and the SNV-SVM model have obtained 100% correct recognition rates (CRRs). The CARS method shows the SG 1st-Der-SVM model having the smallest BIC value, and the model was optimal. [ABSTRACT FROM AUTHOR]

Anil Ananthaswamy's book, "Why Machines Learn: The Elegant Math Behind Modern AI," attempts to explain how artificial intelligence (AI) works by scanning data for patterns and making predictions. However, the book's detailed breakdown of probability, principal component analysis, and eigenvectors may be difficult for readers without an advanced math degree to understand. While there are some interesting examples, such as how statisticians determined the authorship of the Federalist Papers, the book is primarily filled with complex equations and proofs. General readers may find it challenging to follow. [Extracted from the article]

From the spectra alone, we have been able to deduce some interesting things about the behavior of data. We can now extend our data analysis to apply algorithms that include the constituent information as well. This article examines the behavior of data when subjected to the popular principal component regression (PCR) and partial least square (PLS) algorithms. PCR is essentially the same as principal component analysis (PCA), but it takes the results a little further by relating them to known information about the composition or other characteristics of the material being studied. [ABSTRACT FROM AUTHOR]

In our previous column, we introduced a set of data suitable for more extensive examination than was permitted in our previous discussion of the classical least squares (CLS) algorithm. In this column, we start to analyze the data using two methods (CLS and principal component analysis [PCA]) that do not involve the concentrations of the components of the mixtures. We begin our expansion of the analysis by examining the data from the 70-sample data set the same way we did for the previous 15-sample data set. First, we examine the spectral results, and then apply the CLS algorithm to verify the previous findings. Neither of these types of analysis requires the use of external constituent information. We then further expand the analysis by calculating principal components. [ABSTRACT FROM AUTHOR]

The article focuses on a preferred approach to identify and remove outliers in real-time during the production test. It mentions that outlier detection of integrated circuits has been examined in the past as detailed by Stratigopoulos and discussion on outlier detection via machine learning also discuss the identification of outliers at the wafer level.

The article offers information on the two-dimensional gas chromatography which offers significant improvement for volatile chemical separation. Topics include selectivity changes by pressure tuning, replacing one of these dimensions with a two-column ensemble, provided with a pressure tuning makeup gas between them. Article also presents an account of studies that suggest this offers potential for improved operation for a two-dimensional gas chromatography laboratory.

The detection of explosive residues is of crucial interest for security applications such as airport security screenings and terrorist threat assessments. Surface-enhanced Raman spectroscopy (SERS) is a powerful technique that offers high sensitivity and specificity, fast response time, and low limits of detection for various explosives, such as TNT, RDX, and PETN. These explosives can be detected down to picogram levels using a newly developed SERS substrate that consists of a sputtered gold-silver alloy film on a roughened glass surface. This enhancement is achieved with a benchtop integrated Raman instrument using a visible laser excitation at 638 nm, a 20-mW power level, and an acquisition time of only 10 s. Because of the spectral resolution of the Raman technique, the recorded signals allow for both identification of the explosive and quantification of the amount on the substrate. Principal component analysis (PCA) of the Raman spectra successfully showed distinctions among the different explosives. Future experiments will also test the specificity and sensitivity of these SERS substrates for chemical warfare agents, such as sarin or mustard gas. [ABSTRACT FROM AUTHOR]

Here, a method to characterize edible oils and edible oil mixtures through fingerprinting and the isolation of individual analyte differences is reported. Aroma and flavor analytes in extra virgin olive, olive, peanut, grapeseed, and vegetable oils were sampled with headspace solidphase microextraction (HS-SPME). The samples were analyzed with complementary gas chromatography (GC) systems; two-dimensional GC paired with time-of-flight mass spectrometry (GCxGC-TOF-MS) and one-dimensional GC coupled to high resolution TOF-MS. These methods allowed for comparing edible oil varieties by their chromatographic fingerprints, characterizing the samples with the identification of individual analyte differences, differentiating oil mixtures from pure oil varieties with principal component analysis (PCA), and confirming analyte identities with accurate mass data. [ABSTRACT FROM AUTHOR]

The article evaluates the effectiveness of applying infrared (IR) spectroscopy for the diagnosis and treatment of dry eye and cataract. It mentions that IR spectroscopy measures the composition, structure and environment of biomolecules which helps in the study of human lens and tear lipids. It also analyzes the variations in IR spectra by principal component analysis (PCA) and investigates the amount of protein and lipid in meibomian gland secretions.

The article presents the third part of the series article about the equations behind principal components. In this context, the numbering of equations and ways to avoid the use of matrices in the explanations are included. Moreover, the matrix expression corresponding to several of the results being derive.

The author reflects on the difficult approach for principal component analysis, the common methods of multivariate analysis applied to molecular spectroscopy. He comments on the presentation of regression using the matrix formulation as a method of presenting the derivation of the equations needed for multiple linear regression. The author also explains the derivation of principal component algorithm using elementary algebra.

The article presents the fourth part of the discussion series concerning the difficulty of finding principal components. It explains and derives equations in principal components using matrix expression. It likewise attempts to solve the difficulty of finding a function that could maximize approximation variance to a data set.

Part IV of this article series takes a closer look at clustering. Clustering is a useful way of grouping your samples to identify those that are most similar, and is perhaps easier to interpret than principal component analysis, because it provides a single figure retaining most of the initial information. [ABSTRACT FROM AUTHOR]

The article focuses on two electronic circuits which enable indication of a video signal even when no power is supplied to a video device. These circuits show the presence of a video signal on a VGA input. The circuits are aided by the energy of the horizontal synchronization pulses of the video signal itself. These circuits are capable of working with all Video Electronic Standards Association video signals. The first circuit uses the microcontroller PIC10F200. It is a low-cost and low-power device which is very small. The supply voltage which the circuit requires is 2 to 5.5 V. PIC10F200 controls the fading of the LED through pulse-width modulation (PWM). The second circuit uses 555 timers. CMOS version of the timer is to be used to reduce capacitor values and minimize power consumption. U1 is astable which by generating a sawtooth voltage modulates the pulse width of the U2 astable. A 300-nF capacitor (C1) and the current-limiting resistor (R3) integrate U2's PWM output signal to a fading dc voltage.

The article focuses on a study to determine the Drug Antibody Ratio (DAR) by a method based on size-exclusion chromatography (SEC)-multi-angle light scattering (MALS),in conjunction with UV absorption and differential refractive index detection. It mentions that DAR is critical in the designing of antibody-drug conjugates that acts as target-directed therapeutic agents for cancer treatment. The study employs the ASTRA 6 software package for the automation of component analysis.

The article focuses on electronic circuits. A diode-OR connection links a main supply and backup battery to a load. When the backup battery's voltage exceeds the main supply voltage a diode-OR fails to work. IC1 which is an ultra-low-power device handles such a condition. It includes a 1.182-V bandgap reference and a comparator. The backup supply is a 9-V battery and the main switch-mode supply voltage ranges from 7 to 30 V. D1, C1 and R6 cause a delay in the gate drive. This prevents a supply-rail glitch. For proper operation R4 and R3 set the hysteresis in IC1 to 800 mV.