Hyperspectral imaging and machine learning for the prediction of SSC in kiwi fruits

Solids Content (SSC) of the fruits in a non-destructive way. A database is created which includes the hyperspectral data acquired in the visible and near-infrared region (VNIR) and measurements done with a sugar meter.We have applied di?erent machine learning techniques to investigate the correlation between spectral information and the SSC. The models tested were support vector regression (SVR), k-nearest neighbor (KNN), partial least squares (PLS), and multiple linear regression (MLR) with di?erent variable selection techniques and dimensionality reduction. The best model at determining SSC was Uninformative Variable Elimination (UVE)-PLS, which had RMSE = 1.047 °Brix and R2 = 0.39 on the test set

Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The results show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results

Forensic analysis of beverage stains using hyperspectral imaging

Documentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains

Dimensionality reduction and visualisation of hyperspectral ink data using t-SNE

Ink analysis is an important tool in forensic science and document analysis. Hyperspectral imaging (HSI) captures large number of narrowband images across the electromagnetic spectrum. HSI is one of the non-invasive tools used in forensic document analysis, especially for ink analysis. The substantial information from multiple bands in HSI images empowers us to make non-destructive diagnosis and identification of forensic evidence in questioned documents. The presence of numerous band information in HSI data makes processing and storing becomes a computationally challenging task. Therefore, dimensionality reduction and visualization play a vital role in HSI data processing to achieve efficient processing and effortless understanding of the data. In this paper, an advanced approach known as t-Distributed Stochastic Neighbor embedding (t-SNE) algorithm is introduced into the ink analysis problem. t-SNE extracts the non-linear similarity features between spectra to scale them into a lower dimension. This capability of the t-SNE algorithm for ink spectral data is verified visually and quantitatively, the two-dimensional data generated by the t-SNE showed a better visualization and a greater improvement in clustering quality in comparison with Principal Component Analysis (PCA)

Contactless Classification of Strawberry Using Hyperspectral Imaging

Rapid non-contact estimation of fruit quality parameters is an essential factor for an efficient food processing pipeline. We propose a novel workflow for the contactless classification of strawberries based on their sugar content, using Hyperspectral Imaging (HSI) and One-Dimensional Convolutional Neural Network (1D - CNN). Sugar content is an important quality aspect of strawberries, hence classification based on sugar content gives more yield to the fruit producers. We used Visible and Near Infrared (VNIR) hyperspectral camera to acquire HSI data of 50 ripe strawberries and applied the proposed method to classify them. To verify the advantage of the proposed method, the results from 1DCNN are compared against other standard classification methods such as Spectral Angle Mapper (SAM), and Spectral Information Divergence (SID). The results show that the 1D-CNN outperformed other methods by achieving 96.6% classification accuracy

Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document’s paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations

Comparison of Ink Classification Capabilities of Classic Hyperspectral Similarity Features

Ink classification is an active topic in historic and forensic document analysis. In this work, we compared the ink classification capabilities of five commonly used and wellproven similarity measures for classification of hyperspectral imaging (HSI). They are Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), Euclidean Distance (ED), Spectral Information Divergence (SID) and Binary Encoding (BE). These techniques were well explored in different fields of HSI; however, they are not investigated in the field of ink classification. This study reveals the ink classification capabilities of these similarity measures. A combination of different types and colors of inks from different manufactures were used to create sample text. The SAM obtained higher accuracy compared to other methods and also identified that, inks that have nearly similar spectral signatures, cause a decline in accuracies due to misclassification between similar classes