The relationship between perceived freshness and water content of cabbage leaves: A near infrared imaging survey of substance distribution underlying product appearance

An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network

Fruit and vegetable freshness testing can improve the efficiency of agricultural product management, reduce resource waste and economic losses, and plays a vital role in increasing the added value of fruit and vegetable agricultural products. At present, the detection of fruit and vegetable freshness mainly relies on manual feature extraction combined with machine learning. However, manual extraction of features has the problem of poor adaptability, resulting in low efficiency in fruit and vegetable freshness detection. Although exist some studies that have introduced deep learning methods to automatically learn deep features that characterize the freshness of fruits and vegetables to cope with the diversity and variability in complex scenes. However, the performance of these studies on fruit and vegetable freshness detection needs to be further improved. Based on this, this paper proposes a novel method that fusion of different deep learning models to extract the features of fruit and vegetable images and the correlation between various areas in the image, so as to detect the freshness of fruits and vegetables more objectively and accurately. First, the image size in the dataset is resized to meet the input requirements of the deep learning model. Then, deep features characterizing the freshness of fruits and vegetables are extracted by the fused deep learning model. Finally, the parameters of the fusion model were optimized based on the detection performance of the fused deep learning model, and the performance of fruit and vegetable freshness detection was evaluated. Experimental results show that the CNN_BiLSTM deep learning model, which fusion convolutional neural network (CNN) and bidirectional long-short term memory neural network (BiLSTM), is combined with parameter optimization processing to achieve an accuracy of 97.76% in detecting the freshness of fruits and vegetables. The research results show that this method is promising to improve the performance of fruit and vegetable freshness detection.

Transcriptome and hyperspectral profiling allows assessment of phosphorus nutrient status in rice under field conditions

Phosphorus (P) is one of the macronutrients indispensable for crop production, and therefore it is important to understand the potential of plants to adapt to low P conditions. We compared growth and leaf genome-wide transcriptome of four rice cultivars during growth between two fields with different amount of available phosphate and further analysed the acceptable range of P levels for normal growth from the view of both appearance traits and internal P nutrient status, which was measured by profiling the expression of the P indicator gene. This demonstrated that rice plants have a robustness to moderate P-deficient conditions expressing a system for P acquisition and usage without any effects on yield potential and that P indicator gene expression could be a useful index for early diagnosis of P status in plants. To develop a simple method for assessment of P status, we tried to predict the expression level using reflectance spectroscopy and hyperspectral imaging, thereby providing models with good performance. Our findings suggest that rice plants have the potential to adapt to moderate low P conditions in the field and showed that the hyperspectral technique is one of the useful tools for simple measurement of molecular-level dynamics reflecting internal nutrient conditions.

Mechanism for enhancing the growth of mung bean seedlings under simulated microgravity

To elucidate a mechanism for enhancing mung bean seedlings’ growth under microgravity conditions, we measured growth, gene expression, and enzyme activity under clinorotation (20 rpm), and compared data obtained to those grown under normal gravity conditions (control). An increase in fresh weight, water content, and lengths were observed in the clinostat seedlings, compared to those of the control seedlings. Real-time PCR showed that aquaporin expression and the amylase gene were upregulated under clinorotation. Additionally, seedlings under clinorotation exhibited a significantly higher amylase activity. Near-infrared image showed that there was no restriction of water evaporation from the seedlings under clinorotation. Therefore, these results indicate that simulated microgravity could induce water uptake, resulting in enhanced amylase activity and seedling growth. Upregulated aquaporin expression could be the first trigger for enhanced growth under clinorotation. We speculated that the seedlings under clinorotation do not use energy against gravitational force and consumed surplus energy for enhanced growth.

Toward Food Freshness Monitoring: Coordination Binding-Based Colorimetric Sensor Array for Sulfur-Containing Amino Acids

Herein, a self-assembled colorimetric chemosensor array composed of off-the-shelf catechol dyes and a metal ion (i.e., Zn2+) has been used for the sulfur-containing amino acids (SCAAs; i.e., glutathione, glutathione disulfide, L-cysteine, DL-homocysteine, and L-cystine). The coordination binding-based chemosensor array (CBSA) fabricated by a competitive assay among SCAAs, Zn2+ ions, and catechol dyes [i.e., pyrocatechol violet (PV), bromopyrogallol red (BPR), pyrogallol red (PR), and alizarin red S (ARS)] yielded fingerprint-like colorimetric changes. We succeeded in the qualification of SCAAs based on pattern recognition [i.e., a linear discrimination analysis (LDA)] with 100% correct classification accuracy. The semiquantification of reduced/oxidized forms of SCAAs was also performed based on LDA. Furthermore, we carried out a spike test of glutathione in food samples using the proposed chemosensor array with regression analysis. It is worth mentioning that we achieved a 91-110% recovery rate in real sample tests, which confirmed the accuracy of the constructed model. Thus, this study represents a step forward in assessing food freshness based on supramolecular analytical methods.