Independent Modeling Of Class Analogy Research Articles

Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

Kerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq; therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q2 cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines.

A comprehensive tutorial on Data‐Driven SIMCA: Theory and implementation in web

AbstractThe aim of this paper is twofold. First, it serves as a comprehensive tutorial on Data‐Driven Soft Independent Modelling of Class Analogy (SIMCA) (DD‐SIMCA) method for one‐class classification. It covers all practical aspects of developing, validation, and application of DD‐SIMCA models, using a set of simple examples. Second, it introduces web application that implements the main DD‐SIMCA functionality. This application is freely available for everyone and does not require registration or installation. All calculations run locally in a browser without sending any information on a server, hence removing any obstacles to the dissemination of the data and models.

Identifying Meat from Grazing or Feedlot Yaks Using Visible and Near-infrared Spectroscopy with Chemometrics

The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5−year−old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400–2,500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.

Addressing adulteration challenges of dried oregano leaves by NIR HyperSpectral Imaging

Dried oregano leaves are particularly prone to adulteration because of their widespread distribution and their easy mixing with leaves of other plants of lower commercial value, such as olive, myrtle, strawberry tree, or sumac. To reveal the presence of adulteration, in this study we considered an untargeted analytical approach, which instead of involving the a priori selection of specific compounds of interest is focused on defining the characteristic spectral signature of authentic oregano with respect to its most frequent adulterants. NIR HyperSpectral Imaging (NIR-HSI) represents a state-of-the-art, rapid and non-destructive technique, allowing for the collection of both spectral and spatial information from the sample, making it particularly suitable for characterizing visually heterogeneous samples.Authentication issues are typically assessed through class modelling techniques and Soft Independent Modelling of class Analogy (SIMCA) is one of the most used algorithms in this scenario. However, the high variability and heterogeneity within the authentic oregano class resulted in poor outcomes when SIMCA was applied. As an alternative, Soft Partial Least Squares Discriminant Analysis (Soft PLS-DA) algorithm was applied to differentiate authentic oregano samples from pure adulterants. Soft PLS-DA represents a hybrid approach that combines the advantages of both discriminant and class modelling techniques. The resultant classification model has indeed led to promising results, achieving a prediction efficiency of 92.9 %. Finally, based on the percentage of pixels predicted as oregano in the Soft-PLSDA prediction images, a threshold value of 10 % was established, serving as a detection limit of NIR-HSI to distinguish authentic oregano samples from adulterated ones.

Differentiating Structurally Similar Fentanyl Analogs by Comparing Density Functional Theory (DFT) Calculations and Surface-Enhanced Raman Spectroscopy (SERS) Results.

Fentanyl and fentanyl analogs are the main cause of recent overdose deaths in the United States. The presence of fentanyl analogs in illicit drugs makes it difficult to estimate their potencies. This makes the detection and differentiation of fentanyl analogs critically significant. Surface-enhanced Raman spectroscopy (SERS) can differentiate structurally similar fentanyl analogs by yielding spectroscopic fingerprints for the detected molecules. In previous years, five fentanyl analogs, carfentanil, furanyl fentanyl, acetyl fentanyl, 4-fluoroisobutyryl fentanyl (4-FIBF), and cyclopropyl fentanyl (CPrF), gained popularity and were found in 76.4% of the fentanyl analogs trafficked. In this study, we focused on 4-FIBF, CPrF, and structurally similar fentanyl analogs. We developed methods to differentiate these fentanyl analogs using theoretical and experimental methods. To do this, a set of fentanyl analogs were examined using density functional theory (DFT) calculations. The DFT results obtained in this project permitted the assignment of spectral bands. These results were then compared with normal Raman and SERS techniques. Structurally similar fentanyl analogs show important differences in their spectra, and they have been visually differentiated from each other both theoretically and experimentally. Additional results using principal component analysis and soft independent modeling of class analogy show they can be distinguished using this technique. The limit of detection values for FIBF and CPrF were determined to be 0.35 ng/mL and 4.4 ng/mL, respectively, using SERS. Experimental results obtained in this project can be readily implemented in field applications and smaller laboratories, where inexpensive portable Raman spectrometers are often present and used in drug analysis.

Detection of meloxicam residues in milk using ATR- FTIR spectroscopy coupled with chemometrics

Meloxicam (MELX), a non-steroidal anti-inflammatory drug (NSAID), is frequently administered to cattle, poses a risk to human health when it is found in milk. Regulatory bodies of EU, Canada, China and India have prescribed maximum residue limits for MELX in milk as 15, 35 and 15 and 10 μg/kg, respectively. This study explores the use of attenuated total reflectance- Fourier transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques to detect MELX residues in milk. Samples of pure milk and milk samples spiked with MELX at different concentrations ranging from 1 to 50 μg/kg were analysed. ATR-FTIR measurements were taken in the wavenumber range of 4000–400 cm−1 for all the samples. Principal component analysis (PCA) was applied in the regions of 2940–2840 and 1765–1730 cm−1 to differentiate clusters of milk sample with those of MELX and milk spiked with MELX samples. The coefficient of determination (R2) values for calibration and validation sets in wavenumber regions of 2940–2840 and 1765–1730 cm−1 were >0.92 and > 0.94, respectively for the developed partial least squares regression (PLSR) models. All the samples showed 100% classification efficiency using the soft independent modeling of class analogy (SIMCA) technique. The study concluded that the presence of MELX residues can be detected in milk even at 1 μg/kg level using ATR-FTIR and chemometrics. Results obtained from developed FTIR based chemometric method were in agreement with those obtained from LC-MS/MS method and have the potential for application in screening milk samples for the presence of MELX.

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose.

The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system's performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.

The quality of EVOOs through the determination of VOCs coupled with SIMCA analysis

Determining the quality of the oil is essential to discriminate EVOOs from oils labeled as such but not having the chemical and physical characteristics to be EVOOs. The purpose of this study was to determine a metric based on multivariate Soft Independent Modeling of Class Analogy (SIMCA), to identify “Premium quality” versus “Standard quality”, 792 EVOO samples throughout VOCs and sensory analysis data. The result of SIMCA showed a correct classification of the “Premium quality” samples and only few samples were excluded from the SIMCA model (95.4 % sensitivity). The combination of VOCs collected by PTR-ToF-MS and SIMCA analysis allowed us to perfectly distinguish EVOO oils from non-EVOO oils. The proposed method could allow a secure, quick and reliable control of the EVOO quality within the global market.

Uncovering the Counterfeit: A study of whiskey authenticity through volatile organic compound fingerprinting, aroma and color sensory analysis

This study presents a method employing gas chromatography coupled with mass spectrometry and headspace solid-phase microextraction (HS-SPME-GC–MS), supplemented with chemometrics (Soft independent modelling of class analogies - SIMCA), to analyze volatile organic compound (VOCs) profiles in suspect whiskey samples. Furthermore, a sensory analysis of aroma and color was conducted with a panel of 52 non-trained volunteers to evaluate their ability to discriminate and preference for counterfeit whiskeys. The HS-SPME-GC–MS method successfully distinguished 41 seized samples from authentic beverages. Interestingly, sensory analysis revealed that panelists could differentiate between counterfeit and authentic samples with a reference standard but did not consistently show a preference for aroma. In some cases, there was even a preference for the color of counterfeit whiskeys. The findings suggest that sensorial tests alone may not effectively distinguish counterfeit from authentic whiskeys, especially for non-expert consumers, highlighting the need for analytical instrumentation methods in fraud detection.

A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application.

One-class classification (OCC) is discussed in the framework of the measurement and processing of multiway data. Data-driven soft independent modeling of class analogy (DD-SIMCA) is applied in the following formats: (1) multiblock and (2) Tucker 3 N-way SIMCA, which are shown to be useful tools for solving classification tasks. A new decision rule for N-way DD-SIMCA is adopted based on the conventional two-way DD-SIMCA model. Multiblock SIMCA is shown to be useful for variable selection, and Tucker 3 SIMCA to select the optimal model complexity when applying multiway data decomposition and to assess the role of individual samples in the classification model. Both approaches, together with the two-way DD-SIMCA version applied to the unfolded data, are compared regarding the analysis of an experimental data set including genuine and adulterated blueberry extract samples. The latter were employed to produce matrix spectral-time data matrices per sample within a flow injection system, taking advantage of the spectral changes in the sample constituents as a function of the pH of the carrier phase. The need to employ the Tucker 3 model instead of a trilinear decomposition is supported by a discussion on the lack of the trilinearity property of the studied data.

Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

Identification of lentils (Lens culinaris Medik) from Eglouvi (Lefkada, Greece) based on rare earth elements profile combined with chemometrics

An analytical approach has been developed to verify the authenticity of premium lentils originating from Eglouvi, Lefkada, Greece. The method relies on the digestion of samples followed by the analysis of their rare earth elements (REEs) content. Lentils originating from Eglouvi exhibit higher content in most REEs compared to lentils from other regions as well as distinct Sc/Y and Sc/Yb concentration ratios. Principal component analysis effectively segregates “Eglouvi” lentils into a distinct cluster. Soft Independent Modelling of Class Analogy (SIMCA) successfully models “Eglouvi” lentils. Significant enhancement in model specificity was achieved upon inclusion of Sc/Y and Sc/Yb concentration ratios as additional variables. The model is capable of detecting adulteration in blends of Eglouvi lentils, with a minimum rejection threshold of 4.6% w/w for Greek lentil adulterants and 6.0% w/w for imported lentil adulterants.

Identification of geographical and botanical origin of Mediterranean honeys using UV-vis spectroscopy and multivariate statistical analysis

AbstractThe present study aimed to differentiate Mediterranean honey samples with regards to their botanical and geographical origin, using UV-vis spectroscopy, combined with multivariate statistical analysis and chemometrics. A total of 151 commercial honey samples were collected from different countries (Greece, Malta, Spain, Tunisia, and Turkey), which were characterized as thyme, pine, or polyfloral. Discrimination of geographical and botanical origin was achieved successfully with Principal Component Analysis (PCA) together with Random Forest (RF), Partial Least Squares – Discriminant Analysis (PLS – DA), and Data Driven – Soft Independent Modelling of Class Analogies (DD-SIMCA) using the spectral range of 220–550 nm. DD-SIMCA achieved very high accuracy, sensitivity, and specificity on the discrimination of both the geographical and botanical origin of honey samples. The results support the good predictive capability of UV-vis spectroscopy combined with chemometrics for the determination of honey origin. This methodology could be utilized for the rapid and non-destructive assessment of Mediterranean honey origin.

PLS class modelling using error correction output code matrices, entropy and NIR spectroscopy to detect deficiencies in pastry doughs

Biscuits are a highly demanded product worldwide. Its success makes their manufacture process a challenging task, needing new strategies to maintain the high production levels and a high-quality standard. This is determined by two key processes: the kneading and the rolling. This manuscript aims to reflect the improvements that the application of a novel soft multiclass compliant classification method (PLS2-CM) entails regarding the traditional chemometric class modelling. With this new approach, the intention is to detect possible deficiencies in biscuit doughs (excess of water, lack of water or little kneading time) during both industrial processes by using NIR spectroscopy.In PLS2-CM, the coding of the classes is done using an Error Correcting Code Matrix (ECOC), which implies to employ several binary learners so that their number and structure are not predetermined beforehand but are function of the data set to be modelled. The optimization criterion in PLS2-CM is the sensitivity and specificity matrix evaluated by the Diagonal Modified Confusion Entropy (DMCEN), a new index inspired by the Shannon's entropy that is more sensitive to changes in the elements of that matrix than the usual total efficiency. The results obtained according to this index are better with this new soft classification method than the ones obtained when using other soft class modelling techniques such as soft independent modelling of class analogy (SIMCA) or unequal dispersed classes (UNEQ).In this work it is shown that it is possible to completely distinguish a correct kneaded dough from another defective one with a specificity equal to 1 during the kneading process, but the class corresponding with water deficit dough, accepts a very high percentage (80 % in training and 92 % in prediction) of the excess-water dough spectra. Despite that, after the fermentation and during the rolling process, the same doughs are modelled with complete sensitivity and specificity in prediction (100 %), which indicates that the physico-chemical changes produced during the fermentation are decisive to characterize the absence of defects in biscuit doughs kneading by NIR spectroscopy.

Non-Destructive Assessment of Agarwood Resin Formation Status in Aquilaria crassna using Near-Infrared Spectroscopy

Agarwood is a unique and valuable resin synthesized within a few tree species of the tree family Thymelaeaceae primarily as a self-defence mechanism against stress causes such as fungal invasions. Instead, artificial inoculation leads to short-time harvesting practiced in intensive commercial-level cultivations. The extraction of this resin can be done only after the harvesting where accurate estimation of resin yield is crucial for avoiding immature or underdeveloped harvesting. However, the amount of resin accumulated in the trunk cannot be estimated by the outside inspection. As a result, trees are being cut down before they have reached their maximum harvest potential. Therefore, precise detection of fragrant resin formation status inside the tree has become one of the primary challenges in the agarwood production industry. Currently, the methods available are either destructive, inefficient, or inaccurate. As such, this study evaluates the possibility of the use of NIR spectroscopy as a non-destructive and quick detection method for agarwood formations status inside trees in the in-situ mode. Near-infrared (NIR) range (588–1,025 nm) spectra were acquired from resin-formed and non-formed areas from twenty tree trunks of agarwood with five different formation levels. The best agarwood formation areas of logs were assigned as class level 1 and the least was assigned as the class level 5. The spectra were acquired near the opening made for the artificial inoculation process of considered Aquilaria crassna tree trunks. The results of Soft Independent Modelling of Class Analogies showed consistent evidence of differences in the NIR spectral profiles of all levels of agarwood formations. The SIMCA discrimination between the resin developed versus not was achieved with 86% accuracy. The accuracy rates of the degree of the presence of agarwood were obtained as 80%, 100%, 85%, 90%, and 85% respectively, as assigned levels 1 to 5 in the SIMCA models. This research revealed the potential possibility of using NIR technology for the prediction of agarwood resin status inside the agarwood tree in A. crassna before harvesting the tree as a non-destructive and rapid method.
 
 Keywords: NIR spectroscopy, Agarwood, Aquilaria crassna, Non-destructive, Agarwood formation

Non-Destructive Characterization of Italian Local Brassicaceae Cultivars Using ATR-FT-IR and Chemometrics

Brassicaceae is a family of vegetables found all over the world that has been attracting the attention of researchers due to its rich chemical composition and potential health benefits (antioxidant and anti-inflammatory, as well as antimutagenic activity and potential anticarcinogenic effects). In Italy, various Brassicaceae varieties are commercially available, including traditional local cultivars, which have unique features and genetic diversity. As a result, there is a growing need to protect and recognize these landraces to preserve biodiversity. In this study, non-destructive tools such as Attenuated Total Reflectance-Fourier Transform-Infrared Spectroscopy (ATR-FT-IR) and chemometrics were employed to investigate eight distinct Brassicaceae landraces. The collected data were analyzed using a class modeling approach (Soft Independent Modeling of Class Analogy) and a discriminant classification method (Partial Least Squares Discriminant Analysis) to assess similarities and dissimilarities among the samples, all cultivated in an experimental field under the same pedoclimatic conditions. Remarkably, the combination of IR spectra and chemometric tools allowed accurate classification of the samples according only to their genetic background and despite their inclination to hybridization. The study highlights and demonstrates the importance and applicability of this specific non-destructive method for assisting the management and preservation of the genetic resources related to the local varieties of Brassicaceae.

Detection of impurities in m-cresol purple with Soft Independent Modeling of Class Analogy for the quality control of spectrophotometric pH measurements in seawater

Accurate spectrophotometric pH measurements in seawater are critical to documenting long-term changes in ocean acidity and carbon chemistry, and for calibration of autonomous pH sensors. The recent development of purified indicator dyes greatly improved the accuracy of spectrophotometric pH measurements by removing interfering impurities that cause biases in pH that can grow over the seawater pH range to > 0.01 above pH 8. However, some batches of purified indicators still contain significant residual impurities that lead to unacceptably large biases in pH for oceanic and estuarine climate quality measurements. While high-performance liquid chromatography (HPLC) is the standard method for verifying dye purity, alternative approaches that are simple to implement and require less specialized equipment are desirable. We developed a model to detect impurities in the pH indicator m-cresol purple (mCP) using a variant of the classification technique Soft Independent Modeling of Class Analogy (SIMCA). The classification model was trained with pure mCP spectra (350 nm to 750 nm at 1 nm resolution) at pH 12 and tested on independent samples of unpurified and purified mCP with varying levels of impurities (determined by HPLC) and measured on two different spectrophotometers. All the dyes identified as pure by the SIMCA model were sufficiently low in residual impurities that their apparent biases in pH were < 0.002 in buffered artificial seawater solutions at a salinity of 35 and over a pH range of 7.2 to 8.2. Other methods that can also detect residual impurities relevant to climate quality measurements include estimating the impurity absorption at 434 nm and assessing the apparent pH biases relative to a reference purified dye in buffered solutions or natural seawater. Laboratories that produce and distribute purified mCP should apply the SIMCA method or other suitable methods to verify that residual impurities do not significantly bias pH measurements. To apply the SIMCA method, users should download the data and model developed in this work and measure a small number of instrument standardization and model validation samples. This method represents a key step in the development of a measurement quality framework necessary to attain the uncertainty goals articulated by the Global Ocean Acidification Observing Network (GOA-ON) for climate quality measurements (i.e., ±0.003 in pH).

Comparative Study on Handheld, Modular, and Laboratory Raman Instruments for the Analysis of Colon Tissues and Colorectal Polyps

Portable Raman spectrometers may offer advantages for clinical medical diagnostics over laboratory instruments by allowing for quick measurements in the field and provision of data suitable for screening analyses. This work evaluates the potential of using available handheld, modular, and laboratory Raman spectrometers for screening normal colon tissues and benign and malignant colon polyps. The Raman spectra of tissue samples and reference biological macromolecules were measured with these instruments and analyzed using curve fitting and multivariate statistics. The spectra of calf thymus DNA measured with portable devices showed suitable signal-to-noise levels and half-widths of the prominent bands. Band positions, resolution, and relative intensities in the Raman spectra of colon tissues and reference compounds varied for the instruments, and the laboratory device demonstrated the best spectral feature. The principal component analysis (PCA) of the spectra obtained with all Raman devices showed well discrimination of normal colon tissue, adenomatous polyp, and adenocarcinoma. Dendrograms of similarity obtained using hierarchy cluster analysis (HCA) for the Raman spectra of all three devices also showed good separation of these samples. The soft independent modeling of class analogy (SIMCA) and support vector machine (SVM) models efficiently classified normal colon tissues and benign/malignant colorectal polyps based on the Raman data from all three devices. Despite its less pronounced spectral characteristics, the handheld Raman spectrometer can be used in early diagnosis of colorectal carcinoma, comparable to the modular and laboratory instruments.

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.