Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte

The Complexity of Chinese Cereal Vinegar Flavor: A Compositional and Sensory Perspective

With a millennium-long history, traditional Chinese cereal vinegar (CCV) is a significant part of China’s cultural heritage. The unique flavor of CCV is derived from the use of cereal and its bran as raw materials and solid-state fermentation as a brewing technique. This paper systemically summarized recent research progress on the aroma compounds in CCV, the biochemical generation of aroma compounds during the brewing process, and the association between sensory perception and the primary aroma compounds. Furthermore, a complete CCV lexicon and sensory wheel prototype were constructed. This study aims to lay a foundation for future CCV aroma research, quality improvement, and industrialization

A New Technique for Determining Micronutrient Nutritional Quality in Fruits and Vegetables Based on the Entropy Weight Method and Fuzzy Recognition Method

The human body needs nutrients to maintain its regular physiological activity. It requires 40 essential nutrients, including macronutrients (carbohydrates, protein, and fat) and micronutrients (vitamins and minerals). Although macronutrient intake has been improved in China due to people’s increased social awareness, the population’s micronutrient intake remains insufficient. Objective: The current food evaluation system is primarily used to assess macronutrients, while an effective assessment method for micronutrients is still lacking. Fruits and vegetables are low-energy food sources that mainly provide vitamins and minerals and supply the human body with various micronutrients. Methods: In this paper, the entropy and fuzzy recognition methods were used to construct the Vitamin Index (Vitamin Index = Vitamin A Index + Vitamin Comprehensive Index + Vitamin Matching Index) and Mineral Index (Mineral Index = Calcium Index + Mineral Comprehensive Index + Mineral Matching Index) and to evaluate the micronutrient quality of 24 vegetables and 20 fruits. Results: The assessment results showed that Chinese dates displayed the highest Vitamin and Mineral Index among fruits (Vitamin Index = 2.62 and Mineral Index = 2.63), while collard greens had the highest Vitamin Index of the vegetables, at 2.73, and red amaranth had the highest Mineral Index, at 2.74. Conclusions: The study introduces a new method for assessing the nutritional quality of micronutrients, which provides a new idea for assessing the nutrient quality of agricultural products

The Detection of Quality Deterioration of Apple Juice by Near Infrared and Fluorescence Spectroscopy

International audienceProcessing and storage of apple juice often triggers quality deterioration regarding nutritional valuable compounds and unfavourable color changes resulting from browning. Fluorescence and near-infrared (NIR) spectroscopy were applied to detect such quality loss in apple juice. Juice samples were produced from Malus x domestica ’Pinova’, stored at 20 °C for 4 days or heated at 80 °C for 10 min and stored at the same conditions. The quality of apple juice was measured by standard parameters such as soluble solids content, pH, CIE L*, a*, and b* values. Juice fluorescence spectra were recorded with fluorescence excitation at 250, 266, 355, and 408 nm and emission at 280-899 nm resulting in an excitationemission- matrix (EEM) of 1240×4 for each sample. The NIR transmittance spectra were recorded in the wavelength range 900-1350 nm. The often used color b*-value for monitoring browning was correlated with the EEM variation and a reasonable calibration was built by means of n-way partial least squares (N-PLS) regression. The correlation coefficients were >0.9 in all treatments. NIR spectra were sensitive for predicting soluble solids content, but had poor capability to measure the color deterioration. Results indicated that the combination of NIR spectra and fluorescence EEM can be used to monitor the quality deterioration of apple juice

Geographical origin traceability of foxtail millet based on the combination of multi-element and chemical composition analysis

The potential approach of classifying foxtail millet according to geographical origin was investigated using mineral element and chemical composition analysis of samples from various provinces in China. Total 16 mineral elements and five chemical compositions of foxtail millets were analyzed. There were significant differences in 12 elements of millets from different regions. Notable differences were also observed for chemical composition, with Hebei samples showing higher protein content, Henan samples showing higher fat and ash contents and Shandong samples showed higher dietary fiber and amylose contents. Based on the combination of both methods, discriminant analysis provided optimal discrimination among the various geographical origins with a 95.2% classification rate. Our study provides an effective tool to trace the foxtail millet geographic origin through a combination of multi-element and chemical composition analysis

The Determination of Total N, Total P, Cu and Zn in Chicken Manure Using Near Infrared Reflectance Spectroscopy

International audienceIn this study, the 74 chicken manure samples from a chicken farm were used to take the diffuse reflection spectra from 12500 to 4000 cm− 1 by FT-NIR spectrometer. The total N, total P, Cu and Zn of chicken manure samples were predicted by NIR spectra. For the samples of each component, an ascending order was arranged and they were divided into calibration set and prediction set according to their content. Partial least square regression (PLSR) method was applied to construct the calibration model. Results showed that the correlation coefficients of the calibration model for total N was 0.69, the root mean square error of calibration (RMSEC) was 0.66, the root mean square error of prediction (RMSEP) was 0.80. For the models of total P, Cu and Zn, the results were r=0.86, RMSEC=0.29, RMSEP=0.34; r=0.95, RMSEC=3.46, RMSEP=5.71; r=0.94, RMSEC=14.13, RMSEP=25.21, respectively. The results indicated that the NIR spectroscopy was useful to non-destructively determine the content of total N, total P, Cu and Zn of chicken manure. As a complementary detecting method to the conventional analysis, NIR spectroscopy could significantly improve the detecting efficiency