Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set.

The value to students of active learning has been recognized. This has led to the wide use of assignments in statistical methods courses where students use statistical software and computing equipment to analyze data. These assignments enable most students to master the mechanics of data analysis. The amount of experience that a student can get with such assignments is, however, limited. A sizable proportion of students have difficulty grasping some of the many concepts that are introduced in these courses. Nevertheless, these concepts are important for effective modeling and data analysis, and instructors should focus on them. By using current computing technology, it is possible to supplement standard data analysis assignments and algebraic derivations and have students become actively involved in the learning of important statistical concepts. The learning experience can be enhanced by giving students additional statistical “experiences” by using combinations of carefully designed and implemented multiple simulations and dynamic graphics to illustrate key ideas. In this article we describe and illustrate several instructional modules and corresponding software that have been designed to assist instructors in teaching introductory statistics courses.

This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct hyperspectral images from RGB color images. The mean $R$-squared value for hyperspectral image reconstruction was $∼0.98$ in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original hyperspectral images). The results of the study suggested that color-based hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true hyperspectral imaging.

This paper extends work on the construction of instructional modules that use graphical and simulation techniques for teaching statistical concepts ( Marasinghe, et al. 1996 Marasinghe, M. G. , Meeker, W. Q. , Cook, D. , and Shin, T. (1996), “UsingGraphics and Simulation to Teach Statistical Concepts,” TheAmerican Statistician, 50(4), 342–351. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] ; Iversen and Marasinghe 2001 Iversen, P. , and Marasinghe, M. G. (2001), “DynamicGraphical Tools for Teaching Experimental Design and AnalysisConcepts,” The American Statistician, 55(4), 345–351. [Taylor & Francis Online], [Web of Science ®] [Google Scholar] ). These modules consist of two components: a software part and a lesson part. A computer program written in LISP-STAT with a highly interactive user interface that the instructor and the students can use for exploring various ideas and concepts comprises the software part. The lesson part is a prototype document providing guidance for instructors for creating their own lessons using the software module. This includes a description of concepts to be covered, instructions on how to use the module and some exercises. The regression modules described here are designed to illustrate various concepts associated with regression model fitting such as the use of residuals and other case diagnostics to check for model adequacy, the assessment of the effects of transforming the response variable on the regression fit using well-known diagnostic plots and the use of statistics to measure effects of collinearity on model selection.