Near-infrared Hyperspectral Imaging Research Articles

Advances in Emerging Non-Destructive Technologies for Detecting Raw Egg Freshness: A Comprehensive Review

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals.

A broadband hyperspectral image sensor with high spatio-temporal resolution

Hyperspectral imaging provides high-dimensional spatial–temporal–spectral information showing intrinsic matter characteristics1–5. Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible–near-infrared (400–1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.

Non-destructive estimation for Kyoho grape shelf-life using Vis/NIR hyperspectral imaging and deep learning algorithm

Grape shelf-life estimation is a substantial challenge for the grape industry. The objective of this study is to investigate the potential of grape shelf-life estimation using HSI technique and a deep learning algorithm. The visible and near-infrared (400.68–1001.61 nm) hyperspectral reflectance images data of grape samples was acquired and preprocessed with different spectral preprocessing methods. Additionally, a stacked denoising autoencoder (SDAE)-based deep learning algorithm was developed to extract deep features from pixel-level hyperspectral data of grapes, and then these features were used as inputs to establish support vector machine (SVM) models for estimating grape shelf-life. Furthermore, SVM, one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) models were used as traditional machine learning and end to end models for comparison. The results demonstrated that the SDAE-SVM model achieved reasonable recognition accuracy of 100 % and 98.125 % for the shelf-life of grapes in the training and test sets, respectively. The overall results suggested that SDAE-based deep learning method can be used as a powerful tool to deal with large-scale hyperspectral data as well as this research confirms the feasibility of non-destructive estimation for grapes shelf-life by the combination of HSI technique and deep learning method, which would provide a valuable guidance for shelf-life estimation of other postharvest fruit.

Vis-NIR and NIR hyperspectral imaging combined with convolutional neural network with attention module for flaxseed varieties identification

The screening and identifying flax germplasm resources are critical for achieving precise flax breeding and variety enhancement. This study integrates hyperspectral imaging (HSI) technology with deep learning to identify flaxseed varieties. Hyperspectral images were captured for 15 flaxseed varieties across two spectral ranges: Vis-NIR (380–1018 nm) and NIR (870–1709 nm). PCA and LDA were utilized to visually cluster these varieties. To automatically learn the spectral features and improve model performance, a one-dimensional convolutional neural network (CAM-TM-1DCNN) embedded with a channel attention module (CAM) and transformer module (TM) was developed for rapid recognition of flaxseed varieties. Experimental results validate the model's efficacy. Compared with ELM, BPNN, LSTM and 1DCNN classification models, the CAM-TM-1DCNN demonstrated superior classification performance in the NIR spectral range, achieving a test accuracy of 95.26 %. Moreover, all models performed better in the NIR spectral range compared to the Vis-NIR spectral range. The study also evaluated the impact of SPA and CARS feature selection algorithms on the classification models, confirming that the full-spectrum-based CAM-TM-1DCNN model outperformed others. These findings suggest that the CAM-TM-1DCNN model can effectively identify flaxseed varieties, providing a novel strategy and viable technical approach for future flaxseed variety recognition based on HSI technology.

Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest RP2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the RP2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance.

Stand-off hazardous materials identification based on near-infrared hyperspectral imaging combined with convolutional neural network

Near-infrared (NIR) hyperspectral imaging enables rapid, non-contact imaging of hazardous materials in a non-destructive manner, allowing for analysis based on spectral reflection information. However, using traditional methods, it is challenging to identify hazardous materials with less distinct spectral reflection features. This study utilizes a self-built NIR hyperspectral imaging system and proposes a new approach. Using a convolutional neural network (CNN), This allows for the rapid completion of high-throughput spectral screening, marking suspicious spectra at spatial points. we sophisticatedly classified six hazardous material types, generating impactful warning images. The optimized CNN demonstrated superior performance (accuracy 91.08 %, recall 91.15 %, specificity 91.62 %, precision 90.17 %, and 0.924 F1 score) compared to SVM and KNN methods. Our study included multitask validation tests, revealing a sensitive detection of 10 mg/cm2 for ammonium nitrate and trinitrotoluene, capable of identifying over 100 targets simultaneously. By simulating real-world scenarios, we successfully detected hazardous chemicals scattered on the ground and accurately identified these hazardous materials in glass and thin plastic products. Even in situations where clothing obstructed the view, we could still correctly identify hazardous chemicals and generate corresponding warning images. Our system demonstrated precise identification capabilities even amidst complex backgrounds. This method provides an accurate and rapid solution for identifying and locating hazardous chemicals, laying a strong foundation for the next steps in non-contact, long-distance quantitative determination of chemical concentrations. This study highlights the effective application of CNN in non-contact, long-distance classification, and recognition of hazardous materials, paving the way for further scientific and engineering applications.

Precision variety identification of shelled and in-shell pecans using hyperspectral imaging with machine learning

Pecans are essential nuts containing polyunsaturated fatty acids and dietary fiber which offer health benefits to humans. They are exported and sold in both in-shell and shelled forms. However, varietal identification poses a challenge to both producers and processors, which results in variety substitution for economic advantages. The aim of this study was to investigate the efficacy of visible near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging systems (HSI) to accurately identify pecan cultivars (Cape fear, Desirable, Stuart blend, and Sumner). The in-shell and shelled spectra were acquired using VNIR and NIR-HSI at wavelengths 400–1000 nm and 900–1700 nm, respectively. The spectra dimensionality was reduced using principal component analysis (PCA). Thereafter, the selected principal components (PCs) were used to build six machine learning classifiers (Decision Tree, Random Forest, Gradient Boosting, Partial Least Square Discriminant Analysis, Support Vector Machine, and Linear Discriminant Analysis (LDA)) for four-class classification. LDA with and without PCA achieved the highest accuracy for both pecan forms. For shelled pecans, the LDA without PCA achieved 90.59 % and increased to 91.67 % accuracy with PCA on the VNIR spectra, while the LDA without PCA achieved 93.36 % and increased to 93.52 % accuracy with PCA on the NIR spectra. For the in-shell pecans, LDA without PCA achieved 98.59 % and increased to 99.12 % accuracy with PCA on the VNIR spectra, while LDA with and without PCA achieved 98.26 % accuracy for the NIR spectra. Moreover, Successive Projection Algorithm was also implemented for wavelength selection and modeling with satisfactory results. Overall, higher accuracy was achieved in the in-shell pecan. This study revealed the usefulness of HSI systems in identifying pecan varieties.

Rapid determination of oil content of single peanut seed by near-infrared hyperspectral imaging

Oil content is a crucial indicator for evaluating the quality of peanuts. A rapid and non-destructive method to determine oil content of individual peanut seed can provide robust technical support for breeding high-oil-content peanut varieties. In this study, we established a rapid determination method using near-infrared hyperspectral imaging and chemometrics to assess the oil content of single peanut seed. After selecting key wavelengths through competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and random frog (RF), we constructed an oil content calibration model based on partial least squares regression for single peanut seed. Validation results demonstrated that the correlation coefficient was 0.8393 with a root mean square error of 1.7771 in the calibration set, while it was 0.7915 with a root mean square error of 2.2943 in the independent prediction set. Most samples exhibited relative errors below 5%, confirming the reliability of this model in predicting oil content of single peanut seed.

Rapid and nondestructive identification of rice storage year using hyperspectral technology

Rice is the main staple food for more than half of the world's population. Consumption of long-stored rice will have adverse effects on the human body. Here, we proposed the near-infrared (NIR) hyperspectral imaging (HSI) technique to distinguish rice from different storage years. Multiplicative Scatter Correction (MSC), Standard Normalize Variate (SNV) and 1st Derivative (1st) were used for the pretreatment of HSI data. In order to reduce dimensional spectral features, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (tSNE) were used for data visualization. Besides, spectral characteristic wavelengths were extracted by competitive adaptive reweighted sampling (CARS) and least absolute shrinkage and selection operator (Lasso). Simultaneously, the textural features of rice were analyzed by Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM) and Tamura algorithms. In order to realize feature fusion of spectra and texture, Support Vector Machine (SVM) model was optimized using the Whale optimization algorithm (WOA) and Extreme Gradient Boosting (XGBoost) model were established based on spectral features and textural features. Compared to other models, feature fusion model showed an excellent result with an accuracy of 98.89%. Experimental results suggested that HSI technology can be served as an effective method for rice detection.

Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning

Yams (Dioscorea spp.) are important food and commercial crops in West African countries. They contribute significantly to global food production and provide dietary energy. The quality of yam food products depends on specific internal and external parameters, such as the DMC and other biochemical traits. However, measuring these traits can be challenging, particularly when analyzing many genotypes. This study aimed to evaluate the feasibility of using near-infrared (NIR) hyperspectral imaging (932–1721 nm) along with machine learning to rapidly measure the dry matter content (DMC) of fresh, intact yam tubers. Hyperspectral images were acquired across the yam tuber’s cross-sections, and the resulting spectra from the images were averaged and preprocessed. Partial least square regression (PLSR) combined with successive progressions algorithms (SPA), Competitive Adaptive Reweighted Sampling (CARS), Artificial Neural network (ANN) and Boruta algorithms (BA) were used to select the important wavelengths for developing a prediction model for DMC (g/100 g). The PLSR-SPA-CARS model showed the most accurate prediction performances with a coefficient of determinations in calibration (R2cal) and prediction (R2pred) of 0.974 and 0.958, respectively, and low root mean square error (RMSEP) of 0.898 g/100 g. The distribution of DMC was visually represented by projecting the developed model to generate color chemical maps. This study resolves that NIR hyperspectral imaging can rapidly assess the DMC of fresh, intact yam tubers.

Hyperspectral Imaging Technique to Characterize Digestate and Visualize Physical Impurities in Anaerobically Digested Biowaste.

Methods used to monitor anaerobic digestion (AD) indicators are commonly based on wet chemical analyses, which consume time and materials. In addition, physical disturbances, such as floating granules (FGs), must be monitored manually. In this study, we present an eco-friendly, high-throughput methodology that uses near-infrared hyperspectral imaging (NIR-HSI) to build a machine-learning model for characterizing the chemical composition of the digestate and a target detection algorithm for identifying FGs. A total of 732 digestate samples were used to develop and validate a model for calculating total nitrogen (TN), total organic carbon (TOC), total ammonia nitrogen (TAN), and chemical oxygen demand (COD), which are the chemical indicators of responses to disturbances in the AD process. Among these parameters, good model performance was obtained using the dried digestates data set, where the coefficient of determination (R2test) and the root-mean-square error (RMSEtest) were 0.82 and 1090 mg/L for TOC, and 0.86 and 690 mg/L for TN, respectively. Furthermore, the unique spectral features of the FGs in reactors with a lipid-rich substrate meant that they could also be identified by the HSI system. Based on these findings, developing NIR-HSI solutions to monitor the digestate properties in AD plants has great potential for industrial application.

Rapid and low-cost detection of saffron (Crocus sativus L.) adulteration using smartphone videos and spectral data fusion strategy

Saffron (Crocus sativus L.) is the most expensive spice in the world and is highly susceptible to economically motivated adulteration. Hyperspectral imaging (HSI) and near-infrared (NIR) spectroscopy are recently popular techniques for rapid, non-destructive detection of saffron adulteration. However, the high cost and measurement complexity of these techniques make them less suitable for consumer-level applications. In this work, a smartphone video-based system combined with chemometrics is used to detect saffron adulteration. A smartphone is utilised to capture short videos of pure and adulterated saffron powder samples illuminated by its colour-changing screen. The video frames are decomposed into RGB colour images and converted into spectral-like data. Partial least squares regression is used to model the relationship between data and saffron purity. The smartphone video-based system obtains a coefficient of determination of 0.9774 for prediction, which is comparable to reference techniques such as NIR spectroscopy and HSI. Moreover, it is efficient in presenting distribution maps related to saffron purity. In addition, when used in conjunction with the smartphone video-based system, HSI and NIR spectroscopy achieve higher performance without significantly increasing measurement cost and complexity. Such results suggest that the smartphone video-based system has the potential to be a viable primary screening and auxiliary tool for detecting saffron adulteration.

Terahertz time-domain spectro-imaging and hyperspectral imagery to investigate a historical Longwy glazed ceramic

In this paper, we present the potential of Terahertz Time-Domain Imaging (THz-TDI) as a tool to perform non-invasive 3D analysis of an ancient enamel plate manufactured by Longwy Company in France. The THz data collected in the reflection mode were processed using noise filtering procedures and an advanced imaging approach. The results validate the capability to identify glaze layers and the thickness of ceramic materials. To characterize the nature of the pigments, we also use with X-ray images, visible near-infrared hyperspectral imaging spectroscopy, and p-XRF (portable X-ray fluorescence) to qualitatively and quantitively identify the materials used. The obtained information enables a better understanding of the decoration chromogens nature and, thus, to determine the color palette of the artists who produced such decorative object. We also establish the efficiency of a focus, Z-tracker, which enables to perform THz imaging on non-flat samples and to attenuate artifacts obtained with a short focus lens. Then, 3D images are extracted and generated, providing a real vision. We also report the evaluation of the internal damage state through the detection of fractures.

Monitoring of parasite Orobanche cumana using Vis-NIR hyperspectral imaging combining with physio-biochemical parameterson host crop Helianthus annuus.

This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O2-, OH-), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.

Research on non-destructive identification technology of rice varieties based on HSI and GBDT

Accurate identification of rice varieties is of great significance for rice planting, field management and storage, and is also a key link in the process of agricultural breeding. In this study, a gradient boosting decision tree (GBDT) model was established based on hyperspectral imaging (HSI) to realize high-speed and non-destructive variety identification of six rice varieties. In this study, the near-infrared hyperspectral images of 600 rice samples of 6 varieties were taken as the research object, and the characteristic spectra of sensitive regions of the sample spectral images were processed by multiplicative scatter correction (MSC), and after the characteristic wavelengths were determined by the importance scores, the GBDT model to realize the identification of rice sample varieties, and the grid search algorithm was used to optimize the four internal parameters of GBDT. The results showed that the established GBDT model for the accuracy of rice variety identification of vitro test set samples reached 95%, indicating that HSI can be used to quickly and non-destructively identify rice varieties, and provide a new idea for batch online non-destructive testing of rice seeds.

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

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

Simultaneous detection for storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging with multi-target characteristic selection and multi-task model

The detection for storage condition and storage time can ensure quality and safety of yellow peaches, and optimize storage management. This study explored the feasibility of simultaneously detecting the storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging technology combined with multi-target characteristic selection and multi-task model. Firstly, a total of 1080 hyperspectral images of 120 yellow peach samples under different storage conditions and different storage time were acquired using visible near-infrared hyperspectral imaging system. Subsequently, Savitzky-Golay first derivative was used to preprocess the raw spectra to improve the signal-to-noise ratio of the spectra. Distinguished index modified competitive adaptive reweighted sampling (DI-CARS) was proposed to select characteristic wavelengths that simultaneously characterize information of storage condition and storage time. Wild horse optimizer optimized support vector machine (WHO-SVM) was proposed for the multi-task modeling analysis. The results showed that multi-target characteristic selection and multi-task model not only reduced 66.67 % in computational cost, but also improved the accuracy, and multi-task WHO-SVM model based on multi-target DI-CARS achieved the optimal result with the overall accuracy reached 98.06 % and the number of characteristic wavelengths being 99. In conclusion, hyperspectral imaging combined with multi-target DI-CARS and multi-task WHO-SVM is feasible to simultaneously detect storage condition and storage time of yellow peach under different storage conditions.

Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging.

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach.

An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.

Maiolica seen by Vis–NIR hyperspectral imaging spectroscopy: the application of an ultraportable camera at the Museo Nazionale del Bargello

An ultraportable hyperspectral camera operating in the Vis–NIR range (400–1000 nm) was used in this study for the non-invasive analysis of a selection of Italian maiolica wares and sherds from the Museo Nazionale del Bargello in Florence, Italy. The studied objects included authentic archaeological sherds, nineteenth-century forgeries, and ceramics of uncertain origin. The primary aim of this study was to evaluate the efficacy of hyperspectral imaging (HSI) spectroscopy combined with multivariate analysis for examining highly refractive and three-dimensional glazed ceramic artefacts within the constraints of a museum environment, including imperfect lighting conditions. Two data processing pipelines were tested: one based on principal component analysis (PCA) with score plots and the other on spectral angle mapper (SAM) classification obtained through the ENVI Spectral Hourglass Wizard (ENVI-SHW). The analyses enabled the discrimination between original parts and/or restored parts and additions. Among others, a complex assemblage of original sherds, restored parts, and non-ceramic parts was observed in a Renaissance pastiche vase. Furthermore, the method enabled the identification of the various chromophores providing clues on the artistic colour palette. In particular, the identification of chromium in the blue and green glazes of two objects of uncertain chronology suggested that they were not made before the nineteenth century. The acquisition of a larger and more detailed database of HSI data on maiolica glazes is strongly encouraged to improve the applicability of this method in authenticity, art-historical and archaeological studies.