Assessing the ripeness and aging of fruits and vegetables with hyperspectral imaging
Measuring the aging of plums and tomatoes with Specim FX10 hyperspectral camera
Food age is a crucial parameter for quantifying freshness. Within this context, the ripeness and the firmness of fruits and vegetables are the two most essential qualities to be observed and monitored. Hyperspectral cameras enable observation of spectral changes in fruits and vegetables throughout the ripening process.
In this study, we utilized the Specim FX10 hyperspectral camera, mounted on the lab scanner, to inspect plums and tomatoes over 20 days to assess the aging process (Figure 1). The Specim FX10 is a visible-near infrared (VNIR) camera that covers the spectral range from 400 to 1000 nanometers. The first part of the analysis focuses on the spectral features of the samples over time. Creating a regression model for the aging of tomatoes and plums.
Figure 1: A sample of three plums and tomatoes placed on a lab scanner 40×20 and measured with a Specim FX10 camera for 20 days.
Photos of the samples were taken, along with the hyperspectral data. The pictures show that the freshness of the plums, especially the tomatoes, degraded markedly over time (Figure 2). We made a small cut in the middle of one tomato and a plum. This cut seemed to have a substantial impact on accelerating the aging of the tomato but not on the plum.
Figure 2: Photos of the samples taken on the 1st, 13th, and 20th day.
Spectral reflectance reveals chemical changes
A rectangular selection was made on each plum and tomato each day when the spectral measurements were taken (1st, 2nd, 3rd, 6th, 9th, 13th, 14th, 16th, 17th, and 20th day). Only the spectra obtained on the 1st, 13th, and 20th day are presented in Figure 3 to ease the reading of the results. The team averaged the spectra over the selection.
The spectral differences are more significant for tomatoes than for plums. This difference is already visible in the photos taken on the 1st, 13th, and 20th days (Figure 2).
The spectra reveal chemical changes that happen over time within the fruits and vegetables. Plums and tomatoes are green at early growing stages due to the chlorophyll they contain. However, when ripening occurs, the chlorophyll breaks down into another chemical. For tomatoes, chlorophyll breaks down into lycopene, which explains the red color. This chemical change accounts for the spectral variation of the plums and tomatoes over time, between 550 and 750 nanometers. The ripening process of fruits and vegetables also affects their moisture level and structure, impacting their spectra at 970 nanometers. Other properties (e.g., sugar content) also change over time, shaping the spectral reflectance.
Figure 3: False RGB images of the plum and tomatoes acquired on the 1st, 2nd, 3rd, 6th, 9th, 13th, 14th, 16th, 17th, and 20th day. Each dataset was combined into one (mosaic), from the left (1st day) to the right (20th day). Averaged spectra for each tomato and plum are displayed on the 1st day (white), 13th day (pink), and 20th day (purple).
Regression model to quantify the aging
We built a regression model to quantify the aging of plums and tomatoes (Figure 3), using the imaging day as the independent variable in the regression. For the plums, the model achieved an R2 of 0.81, while for the tomatoes, it reached 0.91. We computed these values using selections different from those used to train the model. Figure 4 presents the regression graph comparing actual values to predictions. For the plums, we based the model on a reduced spectral range from 588 to 976 nanometers. For the tomatoes, we used spectral bands ranging from 445 to 993 nanometers.
Figure 4: Regression model output on the three plums (top) and three tomatoes (bottom). Data were acquired on the 1st, 2nd, 3rd, 6th, 9th, 13th, 14th, 16th, 17th, and 20th day (from left to right). The heat map ranges from the 1st day (Minimum) to the 25th day (Maximum).
Conclusion
The Specim FX10 camera is suitable for measuring the ripeness and aging of fruits and vegetables, as it is sensitive to traits related to freshness in agri-food products. When building a typical regression model, laboratory measurements should be used as a reference value to develop and validate the model. However, this is not necessary for accessing the aging of fruits and vegetables.
Hyperspectral cameras operating in the visible-near infrared (VNIR) range provide an efficient tool for monitoring the quality of fresh food products. Hyperspectral imaging is an especially suitable method for food grading, sorting, and classification compared to conventional point-based methods due to its non-destructive nature.
