Modelling postharvest quality of blueberry affected by biological variability using image and spectral data.

BACKGROUND: Hyperspectral reflectance and transmittance sensing as well as near-infrared (NIR) spectroscopy were investigated as non-destructive tools for estimating blueberry firmness, elastic modulus and soluble solid content (SSC). Least squares-support vector machine models were established from these three spectra based on samples from three cultivars viz. Bluecrop, Duke and M2 and two harvest years viz. 2014 and 2015 for predicting blueberry postharvest quality. RESULTS: One-cultivar reflectance models (establishing model using one cultivar) derived better results than the corresponding transmittance and NIR models for predicting blueberry firmness with few cultivar effects. Two-cultivar NIR models (establishing model using two cultivars) proved to be suitable for estimating blueberry SSC with correlations over 0.83. Rp (RMSEp ) values of the three-cultivar reflectance models (establishing model using 75% of three cultivars) were 0.73 (0.094) and 0.73 (0.186), respectively , for predicting blueberry firmness and elastic modulus. For SSC prediction, the three-cultivar NIR model was found to achieve an Rp (RMSEp ) value of 0.85 (0.090). Adding Bluecrop samples harvested in 2014 could enhance the three-cultivar model robustness for firmness and elastic modulus. CONCLUSION: The above results indicated the potential for using spatial and spectral techniques to develop robust models for predicting blueberry postharvest quality containing biological variability. © 2015 Society of Chemical Industry.

[Comparison of Predicting Blueberry Firmness and Elastic Modulus with Hyperspectral Reflectance, Transmittance and Interactance Imaging Modes].

In this study, a imaging system with hyperspectral reflectance, transmittance and interactance was constructed for estimate the firmness and elastic modulus of blueberry. The comparisons of these three imaging modes were carried out. This hyperspectral system could also be applied for scattering modewhile this mode was not suitable for small fruit such as blueberry. The reflectance hypercubes were segmented with the algorithm based on the Otsu method, and the transmittance and interactance hypercubes were processed with the algorithms based on region growing approach. Subsequently, the extracted spectra were pretreated with the Standard Normal Variate (SNV) and Savitzky-Golay of the first derivative (Der), and least squares-support vector machine was applied for the establishment of the corresponding prediction models. The obtained results demonstrated that -reflectance-SNV model could predict blueberry firmness with correlation coefficient of prediction sample set (Rp) of 0.80 and the ratio of percent deviation (RPD) of 1.76 among the models using full spectra. The elastic modulus of blueberry was better estimated by the full transmittance spectra subjected to SNV pretreatment with Rp (RPD) of 0.78 (1.74) than the other models. Furthermore, Random Frog selection approach could to some extent reduce the uninformative wavelengths while increasing the prediction accuracy of the established models. Random Frog-Interactance-Der model achieved Rp (RPD) of 0.80 (1.83) for blueberry firmness, but the number of wavelength was 140. In the case of blueberry elastic modulus, random frog-transmittance-SNV showed the relatively superior performance compared to the other models, with Rp (RPD) of 0.82 (1.83) and fewer wavelength number of 20.

Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement.

We present a dual-mode imaging system operating on visible and long-wave infrared wavelengths for achieving the noncontact and nonobtrusive measurements of breathing rate and pattern, no matter whether the subjects use the nose and mouth simultaneously, alternately, or individually when they breathe. The improved classifiers in tandem with the biological characteristics outperformed the custom cascade classifiers using the Viola­Jones algorithm for the cross-spectrum detection of face and nose as well as mouth. In terms of breathing rate estimation, the results obtained by this system were verified to be consistent with those measured by reference method via the Bland­Altman plot with 95% limits of agreement from ? 2.998 to 2.391 and linear correlation analysis with a correlation coefficient of 0.971, indicating that this method was acceptable for the quantitative analysis of breathing. In addition, the breathing waveforms extracted by the dual-mode imaging system were basically the same as the corresponding standard breathing sequences. Since the validation experiments were conducted under challenging conditions, such as the significant positional and abrupt physiological variations, we stated that this dual-mode imaging system utilizing the respective advantages of RGB and thermal cameras was a promising breathing measurement tool for residential care and clinical applications.