Postharvest Biology and Technology

Here, we used long-read sequencing technology in combination with RNA-seq analysis to investigate the diversity and complexity of the litchi transcriptome, as well as transcripts that are differentially expressed in the four different postharvest stages of litchi fruit. We obtained a reference transcriptome with 50,808 unique full-length isoforms.

Application of edible gum coating supplemented with Ascorbic Acid or CaCl2 on Artichoke Bottoms.

Raw data on melon quality

The file contains data from all the experiments used and listed in the manuscript. The qPCR analysis, calculations and standards are also listed in the file, in different tabs.

The Microsoft Excel Worksheet provided as supplementary data for this article include the following data sets: Mean (n=3) semi-quantitative SPME GC-MS volatile composition data (ng g-1) of fresh-cut and stored watermelon samples and p-value for comparison of various processing and packaging treatments (Table 1), PTR-MS mass fragmentation patterns and relative abundance of chemical reference standards of volatiles found in watermelon (Table 2), and normalized mean (n=2) of PTR-MS data for fresh-cut watermelon samples at different processing, packaging and storage conditions (Table 3).

data about mierals content of floret and stalk

Each folder inside the banana image dataset folder contains a banana tier with its six side images. The number corresponds to its sampleID. There are 194 banana tier subjects with six images corresponding to its side views: front, left, right, back, top and bottom. A total of 1,164 banana images. The banana_features.csv shows the extracted features: RGB (red, green, blue) color values, the image side view, class and finger size. There are four class values with its respective numerical value (1: ‘extra class’, 2: ‘class II’, 3: ‘class I’, 4: ‘reject’) and there are 65, 49, 30 and 50 samples per class, respectively. Note that the finger size feature was taken by manually measuring the length of the top middle finger of a banana tier ("hand") in millimeter (mm). This specific finger was assumed to have a regular size compared to other fingers. This dataset can be used for machine learning classification of banana based on its features. In addition, the available images can be used for automating the manual measurement of the top middle finger size.

This supplementary material explained how the basic modelling tool was used to design the experimental MAP system.

(1)SAE-FNNtrain.PY: Python code of SAE-FNN model; (2)SAE-FNNpredict.PY: use the trained model to predict firmness and SSC; (3)data/PearMeanspectra.csv: 180 mean spectra and the corresponding firmness and SSC; (4)data/train_pixels/ramdonpixel_train.pkl.gz, data/train_pixels/ramdonpixel_val.pkl.gz: random pixel spectra for training the SAE-FNN model; (5)logs/*:trained model files for firmness and SSC prediction.