Seed material
Jatropha plants have a great variation in the fruit ripening time, with the same plant showing fruits at different stages of ripeness [38]. In this study, changes of pericarp color were used as indicators of ripening, and all fruits were collected in the ‘brown dry’ maturity stage [39]. Fruits were obtained from two accessions grown respectively in Canarana, Mato Grosso State, Brazil (13° 33′ 16′′ S 52° 16′ 20′′ W) and in Rio Largo, Alagoas State, Brazil (9° 29′ 41′′ S 35° 51′ 12′′ W).
Three seedlots (Lots 1, 2 and 3) were investigated. Lots 1 and 3 were obtained from the same mother plant grown in Canarana, both collected manually by the farmers: the fruits of Lot 1 were harvested in December 2018 and the fruits of Lot 3 were harvested in February 2019. In Canarana, the mean annual temperature is 24.8 ºC and the mean annual precipitation is 1541 mm, with a dry season from May to July, and a wet season from December to March. In the Köppen-Geiger system the climate of Canarana is classified as Aw. The fruits of Lot 2 were collected manually from field experiments carried out in Rio Largo. In Rio Largo, the mean annual temperature is 24.1 ºC and the mean annual precipitation is 1630 mm, with a dry season from October to February, and a wet season from March to September. In the Köppen-Geiger system the climate of Rio Largo is classified as Am.
After the harvest, fruits were kept at room temperature for one week. Then, seeds were extracted manually from the fruits and each seedlot was homogenized and evaluated for moisture content (fresh weight basis) which ranged from 11.3 to 11.8%. All seedlots were packed in Kraft paper bags and stored at 20 ºC and 40% relative humidity during the experimental period. In this condition, the water content showed reduction in the range of 6.5 to 6.6% among seedlots.
Seed characterization
All seedlots were characterized based on germination and vigor (germination first count, GRI, electrical conductivity and seedling emergence).
Germination tests
The seeds were sown on paper towel and sand substrates and kept at 30 ºC and a photoperiod of 12 hours: a) ten repetitions of 10 seeds per lot were distributed on moistened paper towels (quantity of 2.5 times the dry-paper weight). The seeds were covered with another moist paper towel and rolled up; b) four replications of 25 seeds per lot were sown in moist sand (moistened to 60% of its water holding capacity) in plastic trays. Data were recorded at five (germination first count) and 10 days after sowing; results were expressed as a percentage of normal seedlings per lot. To calculate the germination rate index – GRI [40], the number of emerged seedlings on paper substrate was monitored daily during 10 days.
Electrical conductivity
Four replications of 15 seeds per lot were weighed and placed in plastic cups containing 75 mL of distilled water for six hours. The containers were then covered with cellophane and kept at 25 oC for six hours [41]. The electrical conductivity of the solution was measured by using a DIGIMED DM-32 conductivity meter and the results were expressed in relation to the seed weight (µS cm-1 g-1).
Seedling emergence
Four subsamples of 25 seeds per lot were sown in plastic trays containing sand moistened to 60% of its water holding capacity. Boxes were maintained at room temperature. The percentage of emerged seedlings was determined 10 days after sowing for each seedlot.
The data from each test used for seed characterization were analyzed separately by analysis of variance in a completely randomized design and the means compared by the Tukey’s test (P < 0.05) by the statistical program SAS 9.4 [42]. Prior to ANOVA, all datasets were checked for normality assumptions and did not require any transformation.
Fat and protein content
Proximate chemical composition analysis of the seeds was performed according to the methods of the Association of Official Analytical Chemists [43] for crude fat (AOAC No.4.5.01) and crude protein content (AOAC No.4.2.11).
Multispectral imaging
Seed images were obtained by the VideometerLab4 instrument (Videometer A/S, Herlev, Denmark) and its software VideometerLab version 3.14.9. The instrument is integrated with machine learning and artificial intelligence and it can capture images of reflectance from the samples up to 19 different wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940 e 970 nm), combining them into high-resolution multispectral images (2.192 × 2.192 pixels). Every pixel in the image contains reflectance data, which vary depending on the color, texture and chemical composition.
Before image acquisition, the light setup was adjusted to optimize the light intensity in each wavelength band, resulting in an improved signal-to-noise ratio in such a way that the images captured could be directly comparable. Digital images were captured from both ventral and dorsal surface of 10 samples of 10 seeds per lot. The overview of the ventral and dorsal surfaces of the three seedlots is shown in Fig. 8. Seeds were placed in 90 mm Petri dishes for imaging, and the lid was removed to avoid reflection during digitalization. All images of a sample were captured in one sequence (a few seconds), requiring no sample preparation. The Region of Interest (ROIs) of each seed were extracted into a Binary Large Object (BLOB) toolbox, a built-in function in VideometerLab software, and a transformation of objects versus background was performed to avoid confusion of the results with the variation in the background. Plots of mean spectra were plotted to show the difference among the three seedlots based on their multispectral patters.
A nCDA algorithm was used for discrimination among seedlots. The nCDA is a supervised model based on MSI transformation of the images, in order to minimize the distance to observations within seedlot and to maximize the distance to observations among seedlots. All seed images were collected into a blob database where each blob was a representation of one seed.
We employed a PCA to process the raw data from 19 wavelengths across three seedlots using the package “FactoMiner” [44]. The PCA selected meaningful variables, which were best explained by the first two principal components (PC1 and PC2), according to Pearson’ correlation test at P < 0.05. A biplot graph using PC1 and PC2 (accounting for > 90% of total variance in the data) was built to separate these seedlot groups based on the 19 wavelengths. Afterwards, multispectral data corresponding to only meaningful wavelengths, as previously assigned by the PCA, were evaluated under a multivariate approach using CDA. The CDA was performed with package “candisc” [45], and we tested the effect of vigor level on the multispectral characteristics of jatropha seeds by using a multivariate analysis of variance (MANOVA). In addition, a canonical discriminative plot was obtained to separate the seedlots within a biplot graph. The statistical analyses were performed by VideometerLab software and in the free statistical environment R [46].
X-ray imaging
The radiographic images were generated using the MultiFocus Digital Radiography System (Faxitron Bioptics LLC, USA). The system is equipped with a complementary metal-oxide-semiconductor (CMOS) X-ray sensor coupled with an 11 µm focal spot tube and up to 8X geometric magnification and provides as high as 6 µm resolution for seed imaging with a choice of a 48 µm or 24 µm. The built-in advanced Automatic Exposure Control selects the appropriate exposure time and kV settings for each sample. In total, 100 seeds per lot were radiographed. The X-ray images were evaluated for the internal structure of the seeds and then classified into three different classes.
After X-ray imaging, four repetitions of 25 seeds were sown in sand (moistened to 60% of its water holding capacity) in plastic boxes (32.0 × 28.0 × 10.0 cm) at 30 ºC and photoperiod of 12 hours. At 10 days after sowing, the samples were evaluated for different quality traits: normal seedlings, abnormal seedlings and dead seeds. A CDA transformation and the cluster analysis were performed for quality traits and the meaningful wavelengths obtained in MSI and X-ray classes. For the heat map joined with cluster analysis, original data were normalized prior to turning into distances based on the Euclidean method, as measure of similarity, where close variable or objects are similar and distant objetcs are dissimilar. Euclidean distances were then subjected to hierarchical clustering trees (in rows and columns) considering measurements for seed quality traits, including MSI and X-ray image classes and for seedlots using Ward’s method. Heat map was computed and described using a function of heatmap.2 in ‘gplots’ package [47].