An overview on principle, techniques and application of hyperspectral imaging with special reference to ham quality evaluation and control

Internal quality prediction technology for 'Sulhyang' strawberry fruit using organic analysis and hyperspectral imaging

In recent years, hyperspectral imaging combined with machine learning techniques has garnered significant attention for its potential in assessing fruit maturity. This study proposes a method for predicting strawberry fruit maturity based on the harvest time. The main features of this study are as follows. 1) Selection of wavelength band associated with strawberry growth season; 2) Extraction of efficient parameters to predict strawberry maturity 3) Prediction of internal quality attributes of strawberries using extracted parameters. In this study, experts cultivated strawberries in a controlled environment and performed hyperspectral measurements and organic analyses on the fruit with minimal time delay to facilitate accurate modeling. Data augmentation techniques through cross-validation and interpolation were effective in improving model performance. The four parameters included in the model and the cumulative value of the model were available for quality prediction as additional parameters. Among these five parameter candidates, two parameters with linearity were finally identified. The predictive outcomes for firmness, soluble solids content, acidity, and anthocyanin levels in strawberry fruit, based on the two identified parameters, are as follows: The first parameter, ps, demonstrated RMSE performances of 1.0 N, 2.3 %, 0.1 %, and 2.0 mg per 100 g fresh fruit for firmness, soluble solids content, acidity, and anthocyanin, respectively. The second parameter, p3, showed RMSE performances of 0.6 N, 1.2 %, 0.1 %, and 1.8 mg per 100 g fresh fruit, respectively. The proposed non-destructive analysis method shows the potential to overcome the challenges associated with destructive testing methods for assessing certain internal qualities of strawberry fruit.

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg–Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters

Aggregation-induced heterogeneities in the emission of upconverting nanoparticles at the submicron scale unfolded by hyperspectral microscopy

Transparent upconverting hybrid nanocomposites are exciting materials for advanced applications such as 3D displays, nanosensors, solar energy converters, and fluorescence biomarkers. This work presents a simple strategy to disperse upconverting β-NaYF4:Yb3+/Er3+ or Tm3+ nanoparticles into low cost, widely used and easy-to-process polydimethylsiloxane (PDMS)-based organic-inorganic hybrids. The upconverting hybrids were shaped as monoliths, films or powders displaying in the whole volume Tm3+ or Er3+ emissions (in the violet/blue and green/red spectral regions, respectively). For the first time, hyperspectral microscopy allows the identification at the submicron scale of differences in the hybrids' emission colour, due to variations in the relative intensity of the distinct components of the upconversion spectrum. The effect is attributed to the size distribution of the agglomerates of nanoparticles, highlighting the importance of studying the emission at submicron scales, since this effect is not observable in measurements recorded in larger areas.

Smart storage technologies applied to fresh foods: A review

Fresh foods are perishable, seasonal and regional in nature and their storage, transportation, and preservation of freshness are quite challenging. Smart storage technologies can online detection and monitor the changes of quality parameters and storage environment of fresh foods during storage, so that operators can make timely adjustments to reduce the loss. This article reviews the smart storage technologies from two aspects: online detection technologies and smartly monitoring technologies for fresh foods. Online detection technologies include electronic nose, nuclear magnetic resonance (NMR), near infrared spectroscopy (NIRS), hyperspectral imaging and computer vision. Smartly monitoring technologies mainly include some intelligent indicators for monitoring the change of storage environment. Smart storage technologies applied to fresh foods need to be highly efficient and nondestructive and need to be competitively priced. In this work, we have critically reviewed the principles, applications, and development trends of smart storage technologies.

Sensory and rapid instrumental methods as a combined tool for quality control of cooked ham

In this preliminary investigation, different commercial categories of Italian cooked pork hams have been characterized using an integrated approach based on both sensory and fast instrumental measurements. For these purposes, Italian products belonging to different categories (cooked ham, "selected" cooked ham and "high quality" cooked ham) were evaluated by sensory descriptive analysis and by the application of rapid tools such as image analysis by an "electronic eye" and texture analyzer. The panel of trained assessors identified and evaluated 10 sensory descriptors able to define the quality of the products. Statistical analysis highlighted that sensory characteristics related to appearance and texture were the most significant in discriminating samples belonged to the highest (high quality cooked hams) and the lowest (cooked hams) quality of the product whereas the selected cooked hams, showed intermediate characteristics. In particular, high quality samples were characterized, above all, by the highest intensity of pink intensity, typical appearance and cohesiveness, and, at the same time, by the lowest intensity of juiciness; standard cooked ham samples showed the lowest intensity of all visual attributes and the highest value of juiciness, whereas the intermediate category (selected cooked ham) was not discriminated from the other. Also physical-rheological parameters measured by electronic eye and texture analyzer were effective in classifying samples. In particular, the PLS model built with data obtained from the electronic eye showed a satisfactory performance in terms of prediction of the pink intensity and presence of fat attributes evaluated during the sensory visual phase. This study can be considered a first application of this combined approach that could represent a suitable and fast method to verify if the meat product purchased by consumer match its description in terms of compliance with the claimed quality.

Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network

Peaches in cold storage may develop chill damage, as symptomized by deteriorated texture and lack of juice. To examine fruit quality, we established a hyperspectral imaging system to detect cold injury, and an artificial neural network (ANN) model was developed for which eight optimal wavelengths were selected. Between normal and chill-damaged peaches, significant differences in fruit quality parameters and the spectral response to correlating selected wavelengths were observed. Evidencing this relationship, the correlation coefficients between quality parameters and the respective spectral response of eight selected wavelengths were -0.587 to -0.700, 0.393 to 0.552, 0.510 to 0.751, and 0.574 to 0.773. With optimal representative wavelengths as inputs for the ANN model, the overall classification accuracy of chill damage was 95.8% for all cold-stored samples. The ANN prediction models for quality parameters performed well, with correlation coefficients from 0.6979 to 0.9026. This research demonstrates feasibility of hyperspectral reflectance imaging technique for detecting cold injury.