2-Heptanone, 2-nonanone, and 2-undecanone confer oxidation off-flavor in cow milk storage.

Flavor sensation is one of the most prevalent characteristics of food industries and an important consumer preference regulator of dairy products. So far, many volatile compounds have been identified, and their molecular mechanisms conferring overall flavor formation have been reported extensively. However, little is known about the critical flavor compound of a specific sensory experience in terms of oxidized off-flavor perception. Therefore, the present study aimed to compare the variation in sensory qualities and volatile flavors in full-fat UHT milk (FFM) and low-fat UHT milk (LFM) samples under different natural storage conditions (0, 4, 18, 25, 30, or 37°C for 15 and 30 d) and determine the main component causing flavor deterioration in the FFM and LFM samples using sensory evaluation, electronic nose, and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). In addition, the Pearson correlation between the volatile flavor components and oxidative off-flavors was analyzed and validated by sensory reconstitution studies. Compared with the LFM samples, the FFM samples showed a higher degree of quality deterioration with increased storage temperature. Methyl ketones of odd carbon chains (i.e., 2-heptanone, 2-nonanone, 2-undecanone, 2-tridecanone, and 2-pentadecanone) reached a maximum content in the FFM37 samples over 30 d storage. The combined results of the Pearson correlation and sensory recombination study indicated that 2-heptanone, 2-nonanone, and 2-undecanone conferred off-flavor perception. Overall, the present study results provide potential target components for detecting and developing high-quality dairy products and lay a foundation for specific sensory flavor compound exploration in the food industry.

Rapid discrimination of the identity of infant formula by triple-channel models.

Infant formula is related to children's life and health. However, the existing identification methods for infant formula are time-consuming, costly and prone to environmental pollution. Therefore, a simple, efficient and less polluting identification method for infant formula is urgently needed. The aim of this study was to distinguish between goat and cow infant formula using HS-SPME-GC-MS and E-nose combined with triple-channel models. The results indicated that the main difference of them attributed to thirteen volatile compounds and three sensor variables. Based on this, the linear discriminant and partial least squares discriminant analyses were conducted, and a multilayer perceptron neural network model was constructed to identify the commercial samples. There was a high percentage of correct classifications (>90%) in samples. Together, our work demonstrated that the volatile compounds of infant formula combined with chemometric analysis were effective and rapid for detecting two infant formulas.

A Novel System for Measuring Pterygium's Progress Using Deep Learning.

Pterygium is a common ocular surface disease. When pterygium significantly invades the cornea, it limits eye movement and impairs vision, which requires surgery to remove. It is medically recognized that when the width of the pterygium that invades the cornea is >3 mm, the patient can be treated with surgical resection. Owing to this, this study proposes a system for diagnosing and measuring the pathological progress of pterygium using deep learning methods, which aims to assist doctors in designing pterygium surgical treatment strategies. The proposed system only needs to input the anterior segment images of patients to automatically and efficiently measure the width of the pterygium that invades the cornea, and the patient's pterygium symptom status can be obtained. The system consists of three modules, including cornea segmentation module, pterygium segmentation module, and measurement module. Both segmentation modules use convolutional neural networks. In the pterygium segmentation module, to adapt the diversity of the pterygium's shape and size, an improved U-Net++ model by adding an Attention gate before each up-sampling layer is proposed. The Attention gates extract information related to the target, so that the model can pay more attention to the shape and size of the pterygium. The measurement module realizes the measurement of the width and area of the pterygium that invades the cornea and the classification of pterygium symptom status. In this study, the effectiveness of the proposed system is verified using datasets collected from the ocular surface diseases center at the Affiliated Eye Hospital of Nanjing Medical University. The results obtained show that the Dice coefficient of the cornea segmentation module and the pterygium segmentation module are 0.9620 and 0.9020, respectively. The Kappa consistency coefficient between the final measurement results of the system and the doctor's visual inspection results is 0.918, which proves that the system has practical application significance.

Flexible Pressure Sensor with Micro-Structure Arrays Based on PDMS and PEDOT:PSS/PUD&CNTs Composite Film with 3D Printing.

Flexible pressure sensors are widely used in different fields, especially in human motion, robot monitoring and medical treatment. Herein, a flexible pressure sensor consists of the flat top plate, and the microstructured bottom plate is developed. Both plates are made of polydimethylsiloxane (PDMS) by molding from the 3D printed template. The contact surfaces of the top and bottom plates are coated with a mixture of poly (3,4-ethylenedioxythiophene) poly (styrene sulfonate) (PEDOT:PSS) and polyurethane dispersion (PUD) as stretchable film electrodes with carbon nanotubes on the electrode surface. By employing 3D printing technology, using digital light processing (DLP), the fabrication of the sensor is low-cost and fast. The sensor models with different microstructures are first analyzed by the Finite Element Method (FEM), and then the models are fabricated and tested. The sensor with 5 × 5 hemispheres has a sensitivity of 3.54 × 10-3 S/kPa in the range of 0-22.2 kPa. The zero-temperature coefficient is -0.0064%FS/°C. The durability test is carried out for 2000 cycles, and it remains stable during the whole test. This work represents progress in flexible pressure sensing and demonstrates the advantages of 3D printing technology in sensor processing.

A Flexible Pressure Sensor Based on Magnetron Sputtered MoS2.

Although two-dimensional (2D) layered molybdenum disulfide (MoS2) has widespread electrical applications in catalysis, energy storage, and photodetection, there are few reports available regarding sputtered MoS2 for piezoresistive sensors. In this research, we found that the resistance of magnetron sputtered MoS2 on a flexible substrate changed significantly and regularly when pressure was applied. Scanning electron microscope (SEM) and atomic force microscope (AFM) images revealed an MoS2 micro-grain-like structure comprising nano-scale particles with grooves between the particles. Chemical characterization data confirmed the successful growth of amorphous MoS2 on a polydimethylsiloxane (PDMS) substrate. A micro-thickness film flexible sensor was designed and fabricated. In particular, the sensor with a 1.5 µm thick polydimethylsiloxane (PDMS) substrate exhibited the best resistance performance, displaying a maximum ΔR/R of 70.39 with a piezoresistive coefficient as high as 866.89 MPa-1 while the pressure was 0.46 MPa. A proposed flexible pressure sensor based on an MoS2 film was also successfully used as a wearable pressure sensor to measure plantar pressure and demonstrated good repeatability. The results showed that the thin film pressure sensor had good piezoresistive performance and high sensitivity.

Design and Development of a Fully Printed Accelerometer with a Carbon Paste-Based Strain Gauge.

In this paper, we present a fully printed accelerometer with a piezoresistive carbon paste-based strain gauge printed on its surface, which can be manufactured at low cost and with high efficiency. This accelerometer is composed of two parts: a sensor substrate made from high-temperature resin, which is printed by a 3D printer based on stereolithography apparatus (SLA), and a carbon paste-based strain gauge fabricated by screen-printing technology and by direct ink writing (DIW) technology for the purposes of comparison and optimization. First, the structural design, theoretical analysis, simulation analysis of the accelerometer, and analyses of the conductive mechanism and the piezoresistive mechanism of the carbon paste-based strain gauge were carried out. Then the proposed accelerometer was fabricated by a combination of different printing technologies and the curing conditions of the carbon paste were investigated. After that, the accelerometers with the screen-printed strain gauge and DIW strain gauge were characterized. The results show that the printing precision of the screen-printing process on the sensor substrate is higher than the DIW process, and both accelerometers can perform acceleration measurement. Also, this kind of accelerometer can be used in the field of measuring body motion. All these findings prove that 3D printing technology is a significant method for sensor fabrication and verification.

Research of a Novel 3D Printed Strain Gauge Type Force Sensor.

A 3D printed force sensor with a composite structure developed by combining digital light processing (DLP) based printing and inkjet printing technologies is described in this paper. The sensor has cost effectiveness and time-saving advantages compared to the traditional sensor manufacturing process. During this work, the substrate of the force sensor was printed by a DLP based 3D printer using a transparent high-temperature resin, and the strain gauge of the force sensor was inkjet printed using poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT/PSS) conductive ink. Finite element (FE) simulation was conducted to find the print origin of the strain gauge. The relationship between the mechanical properties of the post-cured resin and the curing time was investigated and the resistance of the printed strain gauges was characterized to optimize process parameters. Afterward, the force sensor was characterized. Experimental results show that the sensitivity of the sensor is 2.92% N-1 and the linearity error is 3.1485% full scale (FS) within the range from 0 mN⁻160 mN, and the effective gauge factor of the strain gauge is about 0.98. The resistance drifting is less than 0.004 kΩ within an hour. These figures prove that the device can perform as a force sensor and 3D printing technology may have great applied potential in sensor fabrication.