Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Producción CientíficaNondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool.Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (project RTI2018-098958-B-I00

A study on non-destructive method for detecting Toxin in pepper using Neural networks

Mycotoxin contamination in certain agricultural systems have been a serious concern for human and animal health. Mycotoxins are toxic substances produced mostly as secondary metabolites by fungi that grow on seeds and feed in the field, or in storage. The food-borne Mycotoxins likely to be of greatest significance for human health in tropical developing countries are Aflatoxins and Fumonisins. Chili pepper is also prone to Aflatoxin contamination during harvesting, production and storage periods.Various methods used for detection of Mycotoxins give accurate results, but they are slow, expensive and destructive. Destructive method is testing a material that degrades the sample under investigation. Whereas, non-destructive testing will, after testing, allow the part to be used for its intended purpose. Ultrasonic methods, Multispectral image processing methods, Terahertz methods, X-ray and Thermography have been very popular in nondestructive testing and characterization of materials and health monitoring. Image processing methods are used to improve the visual quality of the pictures and to extract useful information from them. In this proposed work, the chili pepper samples will be collected, and the X-ray, multispectral images of the samples will be processed using image processing methods. The term "Computational Intelligence" referred as simulation of human intelligence on computers. It is also called as "Artificial Intelligence" (AI) approach. The techniques used in AI approach are Neural network, Fuzzy logic and evolutionary computation. Finally, the computational intelligence method will be used in addition to image processing to provide best, high performance and accurate results for detecting the Mycotoxin level in the samples collected.Comment: 11 pages,1 figure; International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.4, July 201

Identification of internal defects in potato using spectroscopy and computational intelligence based on majority voting techniques

Producción CientíficaPotatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.Ministerio de Ciencia, Innovación y Universidades; Ministerio de Ciencia e Innovación; Agencia Estatal de Investigación y Fondo Europeo de Desarrollo Regional (FEDER) - (grant RTI2018-098156-B-C53

A methodology for sorting haploid and diploid corn seed using terahertz time domain spectroscopy and machine learning

Terahertz technology has been rapidly expanding both in its use and in attention given to it. A possible application is in corn breeding, specifically when the doubled haploid method is used. Haploid kernels are induced in corn plants in order to decrease the time to reach homozygous genetic corn lines. These haploid kernels must be separated from the surrounding diploid kernels; presently this is done by extensive manual labor using visual markers. This work represents a proof of concept that haploid classification can be automated using terahertz time domain spectroscopy (THz-TDS) paired with a machine learning algorithm, like a probabilistic neural network (PNN). In this work, a THz-TDS system was used to collect time domain waveforms from a sample of mixed haploid and diploid corn kernels. Variabilities in beam focus and kernel geometry were reduced by taking multiple scans at different heights and at many scan positions. A watershed image segmentation technique was used to reduce the data quantity and organize them by kernel. The waveform data were then transformed to the frequency domain and further classified by PNN with a training set random subsampling technique. Leave-one-out and K-folds cross-validation procedures were used to train the model. The preliminary results show promise yielding an average classification rate of 75 percent correct by 5-fold cross-validation. THz ability to penetrate material leads to immense potential for similar applications in nondestructive evaluation, biomed, and agriculture

Transformations for non-destructive evaluation of brix in mango by reflectance spectroscopy and machine learning

Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y2 transformation, reflectance R as the independent variable and MSC as a pre-processing technique

Freshness Detection and Classification of Chicken Eggs using Spectroscopy

The poultry industry plays a pivotal role in India's economy, with particular emphasis on egg production. India ranks as the world's third-largest producer of chicken eggs. Eggs are a delicate component of the human diet, and their quality can undergo substantial changes during storage. This has implications for egg quality and the classification of chicken eggs, both of which are critical factors affecting the poultry sector. Globally, numerous chicken breeds are being developed, necessitating the classification of eggs based on breed due to varying atmospheric conditions required for their storage. However, in India, there is a lack of technical methods for classifying eggs from different chicken breeds. The primary challenges faced by the poultry industry in India revolve around maintaining egg freshness and accurately classifying eggs by breed. While developed countries employ grading systems for eggs, this practice is less common in developing nations like India. To address these challenges, this study aims to propose a model that utilizes spectroscopy as a non-destructive method for assessing egg quality and freshness. The model seeks to establish a link between spectral data, collected using a handheld SCiO NIR spectrometer with wavelengths ranging from 740nm to 1070nm at a spectral resolution of 1 nm, and established destructive methods, particularly Haugh Units, to determine egg freshness based on storage duration

Structural Health Monitoring in Composite Structures: A Comprehensive Review.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented

LIFECYCLE MANAGEMENT, MONITORING AND ASSESSMENT FOR SAFE LARGE-SCALE INFRASTRUCTURES: CHALLENGES AND NEEDS

Many European infrastructures dating back to ’50 and ’60 of the last century like bridges and viaducts are approaching the end of their design lifetime. In most European countries costs related to maintenance of infrastructures reach a quite high percentage of the construction budget and additional costs in terms of traffic delay are due to downtime related to the inspection and maintenance interventions. In the last 30 years, the rate of deterioration of these infrastructures has increased due to increased traffic loads, climate change related events and man-made hazards. A sustainable approach to infrastructures management over their lifecycle plays a key role in reducing the impact of mobility on safety (over 50 000 fatalities in EU per year) and the impact of greenhouse gases emission related to fossil fuels. The events related to the recent collapse of the Morandi bridge in Italy tragically highlighted the sheer need to improve resilience of aging transport infrastructures, in order to increase the safety for people and goods and to reduce losses of functionality and the related consequences. In this focus Structural Health Monitoring (SHM) is one of the key strategies with a great potential to provide a new approach to performance assessment and maintenance over the life cycle for an efficient, safe, resilient and sustainable management of the infrastructures. In this paper research efforts, needs and challenges in terms of performance monitoring, assessment and standardization are described and discussed.The networking support of COST Action TU1402 on ‘Quantifying the Value of Structural Health Monitoring’ and of COST Action TU1406 on ‘Quality specifications for roadway bridges, standardization at a European level (BridgeSpec)

Assessment of Geometrical Features of Internal Flaws with Artificial Neural Network Optimized by a Thermodynamic Equilibrium Algorithm

In nondestructive testing (NDT), geometrical features of a flaw embedded inside the material such as its location, length, and orientation angle are critical factors to assess the severity of the flaw and make post-manufacturing decisions. In this study, a novel evolutionary optimization algorithm has been developed for machine learning (ML). This algorithm has been inspired by thermodynamic laws and can be adopted for artificial neural network (ANN). To this end, it was applied to the oscillograms from virtual ultrasonic NDT to estimate geometrical features of flaws. First, a numerical model of NDT specimen was constructed using acoustic finite element analysis (FEA) to produce the ultrasonic signals. The model was validated by comparing the produced signals with the experimental data from NDT tests on the specimens without and with defects. Then, 750 numerical models containing flaws with different locations, lengths, and angles were generated by FEA. Next, the oscillograms produced by the models were divided into 3 datasets: 525 for training, 113 for validation, and 112 for testing. Training inputs of the network were parameters extracted from ultrasonic signals by fitting them to sine functions. The proposed evolutionary algorithm was implemented to train the network. Lastly, to evaluate the network performance, outputs of the network including flaw’s location, length, and angle were compared with the desired values for all datasets. Deviations of the outputs from desired values were calculated by a regression analysis. Statistical analysis was also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in x-location, y-location, length, and angle estimations are 0.09 mm, 0.19 mm, 0.46 mm, and 0.75, with efficiencies of 0.9229, 0.9466, 0.9140, and 0.9154, respectively for the testing dataset, which demonstrates high accuracy in estimation. Results suggest that the proposed AI-based method can be used to characterize flaws with time of flight NDT approach. This research introduces optimized smart ultrasonic NDT as an exact and rapid method in detection of internal flaw, its geometrical features, and also proves the need to replace this method with conventional method which requires interpretation of the human

A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed