Multivariate Classification Methods Research Articles

EEG decoding reveals task-dependent recoding of sensory information in working memory

Working memory (WM) supports future behavior by retaining perceptual information obtained in the recent past. The present study tested the hypothesis that WM recodes sensory information in a format that better supports behavioral goals. We recorded EEG while participants performed color delayed-estimation tasks where the colorwheel for the response was either randomly rotated or held fixed across trials. Accordingly, observers had to remember the exact colors in the Rotation condition, whereas they could prepare for a response based on the fixed mapping between the colors and their corresponding locations on the colorwheel in the No-Rotation condition. Results showed that the color reports were faster and more precise in the No-Rotation condition even when exactly the same set of colors were tested in both conditions. To investigate how the color information was maintained in the brain, we decoded the color using a multivariate EEG classification method. The decoding was limited to the stimulus encoding period in the Rotation condition, whereas it continued to be significant during the maintenance period in the No-Rotation condition, indicating that the color information was actively maintained in the condition. Follow-up analyses suggested that the prolonged decoding was not merely driven by the covert shift of attention but rather by the recoding of sensory information into an action-oriented response format. Together, these results provide converging evidence that WM flexibly recodes sensory information depending on the specific task context to optimize subsequent behavioral performance.

Support vector machine prediction of individual Autism Diagnostic Observation Schedule (ADOS) scores based on neural responses during live eye-to-eye contact

Social difficulties during interactions with others are central to autism spectrum disorder (ASD). Understanding the links between these social difficulties and their underlying neural processes is a primary aim focused on improved diagnosis and treatment. In keeping with this goal, we have developed a multivariate classification method based on neural data acquired by functional near infrared spectroscopy, fNIRS, during live eye-to-eye contact with adults who were either typically developed (TD) or individuals with ASD. The ASD diagnosis was based on the gold-standard Autism Diagnostic Observation Schedule (ADOS) which also provides an index of symptom severity. Using a nested cross-validation method, a support vector machine (SVM) was trained to discriminate between ASD and TD groups based on the neural responses during eye-to-eye contact. ADOS scores were not applied in the classification training. To test the hypothesis that SVM identifies neural activity patterns related to one of the neural mechanisms underlying the behavioral symptoms of ASD, we determined the correlation coefficient between the SVM scores and the individual ADOS scores. Consistent with the hypothesis, the correlation between observed and predicted ADOS scores was 0.72 (p < 0.002). Findings suggest that multivariate classification methods combined with the live interaction paradigm of eye-to-eye contact provide a promising approach to link neural processes and social difficulties in individuals with ASD.

Interpretable classification for multivariate gait analysis of cerebral palsy

BackgroundThe Gross Motor Function Classification System (GMFCS) is a widely used tool for assessing the mobility of people with Cerebral Palsy (CP). It classifies patients into different levels based on their gross motor function and its level is typically determined through visual evaluation by a trained expert. Although gait analysis is commonly used in CP research, the functional aspects of gait patterns has yet to be fully exploited. By utilizing the gait patterns to predict GMFCS, we can gain a more comprehensive understanding of how CP affects mobility and develop more effective interventions for CP patients.ResultIn this study, we propose a multivariate functional classification method to examine the relationship between kinematic gait measures and GMFCS levels in both normal individuals and CP patients with varying GMFCS levels. A sparse linear functional discrimination framework is utilized to achieve an interpretable prediction model. The method is generalized to handle multivariate functional data and multi-class classification. Our method offers competitive or improved prediction accuracy compared to state-of-the-art functional classification approaches and provides interpretable discriminant functions that can characterize the kinesiological progression of gait corresponding to higher GMFCS levels.ConclusionWe generalize the sparse functional linear discrimination framework to achieve interpretable classification of GMFCS levels using kinematic gait measures. The findings of this research will aid clinicians in diagnosing CP and assigning appropriate GMFCS levels in a more consistent, systematic, and scientifically supported manner.

Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images

In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000–2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both “off-line” and “online” fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence.

High-resolution art recognition of modern packaging design and images based on texture image classification

Abstract First, this paper proposes the EPNN algorithm to recognize the high-resolution art of texture images. Then, a multi-scale multivariate image analysis method for color-textured surface classification is established. Regarding probabilistic neural networks, a differential evolutionary algorithm is proposed to optimize the smoothing parameters of basic probabilistic neural networks. Texture feature extraction involves extracting energy and statistical features of texture images using tree-structured wavelet packet decomposition and statistical-based methods. The image classification is performed by estimating similarity distances and classifying typical feature clusters. The results show that in terms of recognition ability, the proposed EPNN in this paper achieves an average improvement of 0.216 in CCP compared to the LBP method. In terms of classification ability, the proposed multi-scale multivariate image analysis color texture surface classification method in this paper generally achieves more than 10% higher classification ability than LGP in CUReT and Outex texture image libraries.

Plasma Expression of Carotid Plaque Presence-Related MicroRNAs Is Associated with Inflammation in Patients with Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is associated with problems beyond the joints such as cardiovascular (CV) disease. MicroRNA-24, -146 and -Let7a are associated with carotid plaque presence in RA patients. We evaluated whether these microRNAs were involved in the inflammatory state of RA, and we studied their gene targets to understand their role in inflammation and atherosclerosis. A total of 199 patients with RA were included. Inflammatory variables such as disease activity score 28 (DAS28) and erythrocyte sedimentation rate (ESR) were quantified. MicroRNAs were extracted from plasma and quantified with qPCR. Multivariate models and classification methods were used for analysis. The multivariate models showed that diminished expression of microRNA-146 was associated with inferior levels of DAS28-ESR, and the decreased expression of microRNA-24, -146 and -Let7a were associated with lowered ESR in the overall cohort. When microRNAs were evaluated globally, a global increase was associated with increased DAS28-ESR and ESR in the overall cohort. Sex-stratified analyses showed different associations of these microRNAs with the inflammatory variables. Finally, random forest models showed that microRNAs have a pivotal role in classifying patients with high and low inflammation. Plasmatic expressions of microRNA-24, -146 and -Let7a were associated with inflammatory markers of RA. These microRNAs are associated with both inflammation and atherosclerosis and are potential therapeutic targets for RA.

Portable Instruments Based on NIR Sensors and Multivariate Statistical Methods for a Semiautomatic Quality Control of Textiles

Near-infrared (NIR) spectroscopy is a widely used technique for determining the composition of textile fibers. This paper analyzes the possibility of using low-cost portable NIR sensors based on InGaAs PIN photodiode array detectors to acquire the NIR spectra of textile samples. The NIR spectra are then processed by applying a sequential application of multivariate statistical methods (principal component analysis, canonical variate analysis, and the k-nearest neighbor classifier) to classify the textile samples based on their composition. This paper tries to solve a real problem faced by a knitwear manufacturer, which arose because different pieces of the same garment were made with “identical” acrylic yarns from two suppliers. The sweaters had a composition of 50% acrylic, 45% wool, and 5% viscose. The problem occurred after the garments were dyed, where different shades were observed due to the different origins of the acrylic yarns. This is a challenging real-world problem for two reasons. First, there is the need to differentiate between acrylic yarns of different origins, which experts say cannot be visually distinguished before garments are dyed. Second, measurements are made in the field using portable NIR sensors rather than in a controlled laboratory using sophisticated and expensive benchtop NIR spectrometers. The experimental results obtained with the portable sensors achieved a classification accuracy of 95%, slightly lower than the 100% obtained with the high-performance laboratory benchtop NIR spectrometer. The results presented in this paper show that portable NIR sensors combined with appropriate multivariate statistical classification methods can be effectively used for on-site textile quality control.

Multi-feature based network for multivariate time series classification

Multivariate time series classification is widely available in several areas of real life and has attracted the attention of many researchers. In recent years, many multivariate time series classification methods have been proposed. However, existing multivariate time series classification methods focus only on local or global features and usually ignore the spatial dependency features among multiple variables. For this, we propose a multi-feature based network (MF-Net). First, MF-Net uses the global-local block to acquire local features through the attention-based mechanism. Next, the sparse self-attention mechanism captures global features. Finally, MF-Net integrates the local features and global features to capture the spatial dependency features using the spatial-local block. Therefore, we can mine the spatial dependency features of multivariate time series while incorporating both local and global features. We conducted experiments on UEA datasets and the experimental results showed that our method achieved performance competitive with that of state-of-the-art methods.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Multivariate decoding methods reveal how speaking two dialects affects executive functions.

How speaking two languages affects executive functions has been a long-standing debate and the mechanisms underlying the observed cognitive advantages of bilingualism remain unspecified. Here, using multivariate pattern classification methods, we decoded spatial patterns of neural signals associated with Flanker task performance in mono-dialectal and bi-dialectal speakers of Chinese. While univariate approach to even-related potentials (ERPs) showed no between-group difference, decoding accuracy of ERPs was reduced in bi-dialectal as compared to mono-dialectal speakers in both congruent-neutral and incongruent-neutral classifications. There was no effect of bidialectalism, however, on decoding accuracy of alpha-band oscillations, an electrophysiological index implicated in inhibition. Behavioural data analysed using the Drift Diffusion Model (DDM) showed facilitating effects of bidialectalism on non-decision times but no effect on drift rates. These findings demonstrate that using two dialects on a daily basis enhances general attentional deployment rather than affecting specific component of executive functions such as inhibitory control. Given that the two dialects of Chinese differed almost exclusively in phonology, the bidialectalism effect was most likely motivated by resolving phonological competition at lexical processing level.

Przekształcenia normalizacyjne danych w procedowaniu metod statystycznych wykorzystywanych w badaniach nad edukacją

Introduction : Sometimes the mapping of the variable values made during the measurement needs to be transformed due to the conditions related to the data analysis. Research Aim : The aim of this article is to present the issue of normalization transformation and the circumstances of their application. Evidence-based Facts : Due to lack of knowledge or experience, transformations are not taken into account in data analysis in education research. And if they do occur, among the most popular methods of data transformation, are chosen those which reduce the skewness of distributions. However, the method of transformation is not always chosen adequately to the properties of the data and the conditions of analysis. Normalization transformations are among the simpler yet effective solutions for preparing data for analysis. Summary : Normalization transformations minimize the risk of artifacts due to differences in orders of magnitude and units of measurement. This is particularly important when conducting analyses using multidimensional scaling and multivariate classification methods.

Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression.

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.

Evaluation of FT-IR spectroscopy combined with SIMCA and PLS‑DA for detection of adulterants in pistachio butter

This work scrutinized the adulteration of pistachio butter with three potential edible oils using Fourier transform infrared spectroscopy (FT-IR) and multivariate classification methods. Each of the classes, including non-adulterated samples and adulterated samples consisting of pistachio butter mixed with various concentrations of peanut oil, corn oil and sunflower oil, were classified. For this purpose, multivariate methods, including soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA), were applied to classify the FTIR data. After evaluating the model on unknown samples, the results indicated that PLS-DA was better than the SIMCA model. The classification efficiency of 98% was obtained using the PLS-DA algorithm compared to the SIMCA model with 91% efficiency for prediction. According to the results FTIR spectroscopy, accompanied by multivariate classification methods, can be used as a rapid and reliable method for classifying and predicting adulteration in pistachio butter with edible oils.

Dynamic and stable population coding of attentional instructions coexist in the prefrontal cortex

A large body of recent work suggests that neural representations in prefrontal cortex (PFC) are changing over time to adapt to task demands. However, it remains unclear whether and how such dynamic coding schemes depend on the encoded variable and are influenced by anatomical constraints. Using a cued attention task and multivariate classification methods, we show that neuronal ensembles in PFC encode and retain in working memory spatial and color attentional instructions in an anatomically specific manner. Spatial instructions could be decoded both from the frontal eye field (FEF) and the ventrolateral PFC (vlPFC) population, albeit more robustly from FEF, whereas color instructions were decoded more robustly from vlPFC. Decoding spatial and color information from vlPFC activity in the high-dimensional state space indicated stronger dynamics for color, across the cue presentation and memory periods. The change in the color code was largely due to rapid changes in the network state during the transition to the delay period. However, we found that dynamic vlPFC activity contained time-invariant color information within a low-dimensional subspace of neural activity that allowed for stable decoding of color across time. Furthermore, spatial attention influenced decoding of stimuli features profoundly in vlPFC, but less so in visual area V4. Overall, our results suggest that dynamic population coding of attentional instructions within PFC is shaped by anatomical constraints and can coexist with stable subspace coding that allows time-invariant decoding of information about the future target.

A panel of plasma microRNAs improves the assessment of surrogate markers of cardiovascular disease in rheumatoid arthritis patients.

Patients with RA present increased risk of cardiovascular (CV) disease compared with the general population. Moreover, CV risk factors that have a causal relationship with atherosclerosis do not seem to fully explain the accelerated process that they exhibit. We evaluated the association of a 10 microRNAs panel with surrogate markers of subclinical arteriosclerosis [carotid intima-media thickness (cIMT), carotid plaque presence (cPP), pulse wave velocity (PWV) and distensibility] in a cohort of RA patients. A total of 199 patients with RA were included. Surrogate markers of arteriosclerosis were measured with My Lab 60 X-Vision sonographer. MicroRNAs were extracted from plasma and quantified with qPCR. Multivariate models and classification methods were performed. Multivariate models showed that microRNAs-24 (β = 15.48), 125a (β = 9.93), 132 (β = 11.52), 146 (β = 15.12), 191 (β = 13.25) and 223 (β = 13.30) were associated with cIMT globally. MicroRNA-24 [odds ratio (OR) = 0.41], 146 (OR = 0.36) and Let7a (OR = 0.23) were associated with cPP in men. Including the microRNAs in a partial least square discriminant analysis model properly classified men with and without cPP. MicroRNA-96 (β = -0.28) was associated with PWV in male patients. Finally, several miRNAs were also associated with cIMT, cPP and arterial stiffness in the high DAS28 group and in the earlier tertile groups of disease duration. Plasmatic expression of microRNA-24, 96, 103, 125a, 132, 146, 191, 223 and Let7a were associated with surrogate markers of CV disease and could be predictors of CV risk in patients with RA.

ASBESTOS DETECTION IN CONSTRUCTION AND DEMOLITION WASTE ADOPTING DIFFERENT CLASSIFICATION APPROACHES BASED ON SHORT WAVE INFRARED HYPERSPECTRAL IMAGING

Asbestos has been widely used in many applications for its technical properties (i.e. resistance to abrasion, heat and chemicals). Despite its properties, asbestos is recognized as a hazardous material to human health. In this paper a study, based on multivariate analysis, was carried out to verify the possibilities to utilize the hyperspectral imaging (HSI), working in the short-wave infrared range (SWIR: 1000-2500 nm), to detect the presence of asbestos-containing materials (ACM) in construction and demolition waste (CDW). Multivariate classification methods including classification and regression tree (CART), partial least squares-discriminant analysis (PLS-DA) and correcting output coding with support vector machines (ECOC-SVM), were adopted to perform the recognition/classification of ACM in respect of the other fibrous panels not containing asbestos, in order to verify and compare Efficiency and robustness of the classifiers. The correctness of classification results was confirmed by micro-X-ray fluorescence maps. The results demonstrate as SWIR technology, coupled with multivariate analysis modeling, is a quite promising approach to develop both “off-line” and “on-line” fast reliable and robust quality control strategies, finalized to perform a first evaluation of the presence of ACM.

Optical Multisensor System Based on Lanthanide(III) Complexes as Near-Infrared Light Sources for Analysis of Milk

Optical multisensor systems are easy-to-use and inexpensive analytical devices. In this work, we propose an optical multisensor system based on the luminescence of Nd(III) and Yb(III) complexes in the near-infrared (NIR) spectral region. The observed emission bands play the role of secondary light sources for further analysis of milk—for the determination of fat content and for the recognition of adulteration. The samples for analysis were prepared by putting a drop of milk upon a thin glass covering the powdered mixture of lanthanide complexes, which were excited by a light-emitting diode (LED) in the ultraviolet region (the maximum intensity at 365 nm). The diffuse-reflectance spectra of samples were acquired in the short-wave NIR range 750–1100 nm using a portable NIR spectrometer. The developed optical system was tested using two sets of milk samples with varying concentration levels of fat and added urea. The obtained spectral data were analyzed using a number of multivariate prediction and classification methods of chemometrics and the results were statistically compared. The regression and classification model performances achieved in this proof-of-concept study illustrate the feasibility of the optical multisensor analysis based on luminescent light sources in the short-wave NIR range, in particular, for their application in the dairy.

Performance of predictive supervised classification models of trace elements in magnetite for mineral exploration

Magnetite is a reliable indicator mineral for exploration because it records petrogenetic processes and discriminate deposit types. Binary discriminant diagrams have limits to accurately predict deposit types (prediction accuracy ~40%). This study aims to determine the best predictive supervised multivariate classification method using the geochemical composition of magnetite in order to provide a model usable by industry and government for mineral exploration. After screening a comprehensive database of ~30 k magnetite analyses, ~17 k observations are selected for our study. These data are from 303 different deposits that belong to 9 major deposit types (BIF, Fe-Ti, IOCG, IOA, Ni-Cu-PGE, Porphyry, VMS, Skarn, V) and a varied class of non-mineralized rocks (Country rocks). We tested the three most promising supervised machine learning algorithms on our dataset (Naive Bayes, K-Nearest Neighbor, Random Forest) with 2 open source statistical platforms: Orange and R. The Random Forest (RF) algorithm yield the best predictive outcome on untransformed data with prediction accuracies of 0.80 with Orange and 0.81 with R. We also tested our RF model on three case studies: 1) IOCG-like deposits, 2) porphyry Cu-Au and Cu skarn, and 3) Scandinavian Ni-Cu-PGE deposits. Our model was able to effectively predict the deposit type for the first two case studies to the large family of Porphyry/IOCG/Skarn. For the 3rd case study, almost 61% of the observations were correctly identified as belonging to Ni-Cu-PGE deposits. Our RF model is therefore accurate enough to be used with confidence for mineral exploration.

The Geography of Women’s Empowerment in West Africa

Women’s empowerment has been a subject of interest because of its relevance to development and demography, particularly in West Africa. Women’s empowerment is typically conceptualized as an individual attribute of women, associated with socioeconomic and demographic characteristics. However, we hypothesize a geography of women’s empowerment in the West African region, where empowerment processes are culturally situated and embedded in place. Such a geography would be observable via spatial associations over the region. This study uses Demographic and Health Survey data from 14 West African states over the past decade and an innovative multi-stage approach combining advanced statistical methods and spatial assessment to analyze indicators of women’s empowerment and its spatial variability across the West African region. First we use a multivariate classification method to identify patterns in responses to empowerment questions and derive an empowerment classification scheme. Next we use these classifications to render a map of West Africa depicting the spatial variation of women’s empowerment in the region. Ultimately, we fit multinomial structured geo-additive regression models to the data to analyze spatial variation in women’s empowerment while controlling for certain socioeconomic-demographic characteristics. Our results demonstrate that women’s responses to empowerment survey questions indeed vary geographically, even when controlling for individual socioeconomic-demographic attributes. This finding suggests that women’s empowerment may relate to aspects of culture embedded in place in addition to the ways it relates to socioeconomic and demographic characteristics.

Contaminant detection in pistachio nuts by different classification methods applied to short-wave infrared hyperspectral images

In this paper different multivariate classification methods applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000–2500 nm) were evaluated for quality control of pistachio nuts. In more detail, the detection of contaminants in edible pistachio products was assessed. Six material classes (i.e. edible and inedible pistachio nuts, pistachio shells, pistachio husks, twigs, and stones) were investigated. Samples were divided into two groups: a training set and a validation set. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Partial Least Squares-Discriminant Analysis (PLS-DA), Principal Component Analysis with Discriminant Analysis (PCA-DA), Principal Component Analysis with k-Nearest Neighbor (PCA-kNN), and Classification And Regression Tree (CART). The classification results obtained for the four models in terms of prediction maps and values of the performance parameters ( Sensitivity , Specificity, and Efficiency ) were good in most cases. The main misclassification errors occur between ‘Edible pistachio’ and ‘Inedible pistachio’ classes and between ‘Pistachio shell’, ‘Pistachio husk’, and ‘Twig’ classes, confirming the similar spectral features of such classes observed by PCA. PCA-kNN provided the best discrimination ability in prediction, with Efficiency values from 0.92 to 0.99, followed by PLS-DA and PCA-DA. The performance of CART decreased from the calibration to the validation phase. The overall results demonstrated that SWIR-HSI technology, coupled with multivariate analysis modeling, is a promising approach to develop both ‘off-line’ and ‘on-line’ fast, reliable and robust contaminant detection in edible pistachios. • Pistachio quality control by short-wave infrared hyperspectral imaging. • Chemometric strategies applied to hyperspectral images for contaminant detection in pistachio nuts. • Detection of edible pistachio and different contaminants in automated and non-destructive way. • Comparison of different classification models for pistachios and contaminants identification. • Identification of a robust and efficient strategy for quality control of edible pistachio.