• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.384-392
• /
• 2022

The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R²: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.

• Horticultural Science & Technology
• /
• v.32 no.1
• /
• pp.105-114
• /
• 2014

We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients ($R^2$) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.

• Korean Journal of Food Science and Technology
• /
• v.50 no.2
• /
• pp.152-164
• /
• 2018

Fermented seasoning pastes were prepared by Aspergillus oryzae and Bacillus subtilis using three edible insects, Tenebrio molitor larvae (TMP), Gryllus bimaculatus (GBP), and Bombyx mori pupa (SPP), with soybean (SBP) as a negative control. Volatile compounds were extracted by the headspace solid-phase microextraction (HS-SPME) method and confirmed by gas chromatograph-mass spectrometry (GC-MS). In total, 121 volatiles from four samples were identified and sub-grouped as 11 esters, 18 alcohols, 23 aldehydes, 5 acids, 10 pyrazines, 2 pyridines, 7 aromatic hydrocarbons, 10 ketones, 19 alkanes, 9 amides, 4 furans and 3 miscellaneous. TMP, GBP, SPP and SBP had 48, 54, 36, and 55 volatile compounds, respectively. Overall, 2,6-dimethylpyrazine and trimethylpyrazine were found by a high proportion in all samples. Tetramethylpyrazine, a main flavor of doenjang, a Korean fermented seasoning soybean paste, was identified as one of the major compounds in TMP, SPP, and SBP. SBP had benzaldehyde, hexanal, n-pentanal, and aldehydes and SPP with pyrazines.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_1
• /
• pp.559-570
• /
• 2017

A partial least squares regression (PLSR) model was developed to map the internal soluble solids content (SSC) of apples using a ground-based hyperspectral scanner that could simultaneously acquire outdoor data and capture images of large quantities of apples. We evaluated the applicability of various preprocessing techniques to construct an optimal prediction model and calculated the optimal band through a variable importance in projection (VIP)score. From the 515 bands of hyperspectral images extracted at wavelengths of 360-1019 nm, 70 reflectance spectra of apples were extracted, and the SSC ($^{\circ}Brix$) was measured using a digital photometer. The optimal prediction model wasselected considering the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP) and coefficient of determination of prediction $r_p^2$. As a result, multiplicative scatter correction (MSC)-based preprocessing methods were better than others. For example, when a combination of MSC and standard normal variate (SNV) was used, RMSECV and RMSEP were the lowest at 0.8551 and 0.8561 and $r_c^2$ and $r_p^2$ were the highest at 0.8533 and 0.6546; wavelength ranges of 360-380, 546-690, 760, 915, 931-939, 942, 953, 971, 978, 981, 988, and 992-1019 nm were most influential for SSC determination. The PLSR model with the spectral value of the corresponding region confirmed that the RMSEP decreased to 0.6841 and $r_p^2$ increased to 0.7795 as compared to the values of the entire wavelength band. In this study, we confirmed the feasibility of using a hyperspectral scanner image obtained from outdoors for the SSC measurement of apples. These results indicate that the application of field data and sensors could possibly expand in the future.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.46 no.7
• /
• pp.833-840
• /
• 2017

For wide application of perilla leaf, which has various healthy functions and can be easily cultured across Korea, the physicochemical and sensory properties of pan bread made with various amounts of squeezed perilla leaf juice (SPLJ) were analyzed. When dough characteristics were analyzed by using farinograph, consistency and dough development time were not significantly different between the control and bread dough made with various amounts of SPLJ, whereas dough stability time increased with increasing SPLJ amount. Expansion rate of dough decreased with increasing SPLJ amount. The volume, specific volume, and baking loss rate of pan bread made with various SPLJ amounts decreased with increasing SPLJ amount. Pan bread crumb colors became thickened and greenish with increasing SPLJ amount. For physical properties of pan bread made with various SPLJ amounts, springiness and cohesiveness decreased with increasing SPLJ amount, whereas brittleness, chewiness, and hardness increased with increasing SPLJ amount. In the sensory strength analysis, pore uniformity and soft texture decreased with increasing SPLJ amount, whereas crumb color (dark greenish), perilla leaf odor, perilla leaf taste, and chewing texture increased with increasing SPLJ amount. In the overall acceptance analysis, 1.5% SPLJ was the most preferred with a score of 7.10. However, statistical differences between 1.5% and 1.0% SPLJ were not detected at P<0.05. In the partial least squares analysis, consumers liked bread with a green crumb color, perilla leaf odor, perilla leaf taste, and soft and chewing texture. In conclusion, physicochemical properties of pan bread made with SPLJ were less desirable than those of the control; however, consumer acceptance of pan bread made with 1.5% SPLJ was shown the highest. Therefore, methods for increasing physicochemical properties of pan bread made with SPLJ need to be developed for wide application of perilla leaf.