Colorimetric Sensor Array for White Wine Tasting.

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

Integrating non-invasive VIS-NIR and bioimpedance spectroscopies for stress classification of sweet basil (Ocimum basilicum L.) with machine learning.

Plant stress diagnosis is essential for efficient crop management and productivity increase. Under stress, plants undergo physiological and compositional changes. Vegetation indices obtained from leaf reflectance spectra and bioimpedance spectroscopy provide information about the external and internal aspects of plant responses, respectively. In this study, bioimpedance and vegetation indices were noninvasively acquired from sweet basil (Ocimum basilicum L.) leaves exposed to three types of stress (drought, salinity, and chilling). Integrating the vegetation index, a novel approach, contains information about the surface of plants and bioimpedance data, which indicates the internal changes of plants. The fusion of these two datasets was examined to classify the types and severity of stress. Among the eight supervised machine learning models (three linear and five non-linear), the support vector machine (SVM) exhibited the highest accuracy in classifying stress types. Bioimpedance spectroscopy alone exhibited an accuracy of 0.86 and improved to 0.90 when fused with vegetation indices. Additionally, for drought and salinity stresses, it was possible to classify the early stage of stress with accuracies of 0.95 and 0.93, respectively. This study will allow us to classify the different types and severity of plant stress, prescribe appropriate treatment methods for efficient cost and time management of crop production, and potentially apply them to low-cost field measurement systems.

Individual finger synchronized robot-assisted hand rehabilitation in subacute to chronic stroke: a prospective randomized clinical trial of efficacy.

OBJECTIVE: To evaluate individual finger synchronized robot-assisted hand rehabilitation in stroke patients. DESIGN: Prospective parallel group randomized controlled clinical trial. SUBJECTS: The study recruited patients who were ≥18 years old, more than three months post stroke, showed limited index finger movement and had weakened and impaired hand function. Patients with severe sensory loss, spasticity, apraxia, aphasia, disabling hand disease, impaired consciousness or depression were excluded. INTERVENTIONS: Patients received either four weeks (20 sessions) of active robot-assisted intervention (the FTI (full-term intervention) group, 9 patients) or two weeks (10 sessions) of early passive therapy followed by two weeks (10 sessions) of active robot-assisted intervention (the HTI (half-term intervention) group, 8 patients). Patients underwent arm function assessments prior to therapy (baseline), and at 2, 4 and 8 weeks after starting therapy. RESULTS: Compared to baseline, both the FTI and HTI groups showed improved results for the Jebsen Taylor test, the wrist and hand subportion of the Fugl-Meyer arm motor scale, active movement of the 2nd metacarpophalangeal joint, grasping, and pinching power (P < 0.05 for all) at each time point (2, 4 and 8 weeks), with a greater degree of improvement for the FTI compared to the HTI group (P < 0.05); for example, in Jebsen Taylor test (65.9 ± 36.5 vs. 46.4 ± 37.4) and wrist and hand subportion of the Fugl-Meyer arm motor scale (4.3 ± 1.9 vs. 3.4 ± 2.5) after eight weeks. CONCLUSIONS: A four-week rehabilitation using a novel robot that provides individual finger synchronization resulted in a dose-dependent improvement in hand function in subacute to chronic stroke patients.

The effects of removable denture on swallowing.

OBJECTIVE: To investigate the relationship between removable dentures and swallowing and describe risks. METHODS: Twenty-four patients with removable dentures who were referred for videofluoroscopic swallowing study (VFSS) were enrolled. We evaluated the change of swallowing function using VFSS before and after the removal of the removable denture. The masticatory performance by Kazunori's method, sensation of oral cavity by Christian's method, underlying disease, and National Institutes of Health Stroke Scale for level of consciousness were collected. Functional dysphagia scales, including the oral transit time (OTT), pharyngeal transit time (PTT), percentage of oral residue, percentage of pharyngeal residue, oropharyngeal swallow efficiency (OPSE), and presence of aspiration were measured. RESULTS: Four patients dropped out and 20 patients were analyzed (stroke, 13 patients; pneumonia, 3 patients; and others, 4 patients). The mean age was 73.3±11.4 years. There were significant differences before and after the removal of the denture for the OTT. OTT was significantly less after the removal of the denture (8.87 vs. 4.38 seconds, p=0.01). OPSE increased remarkably after the removal of the denture, but without significance (18.24%/sec vs. 25.26%/sec, p=0.05). The OTT and OPSE, while donning a removable denture, were correlated with the masticatory performance (OTT, p=0.04; OPSE, p=0.003) and sensation of oral cavity (OTT, p=0.006; OPSE, p=0.007). CONCLUSION: A removable denture may have negative effects on swallowing, especially OTT and OPSE. These affects may be caused by impaired sensation of the oral cavity or masticatory performance induced by the removable denture.