Proximate Content Monitoring of Black Soldier Fly Larval (Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging

The fifth instar live larvae of the black soldier fly (H. illucens) used in this study were purchased 2 kg from Entomo, a Chung-Ju, Korea. They were divided into nine groups of 200 g each and stored frozen at –20°C until just before the experiment. The experimental design was a 3×3 factorial design with three different drying temperatures (50°C, 60°C, and 70°C) and three different drying times (1 h, 2 h, and 3 h), resulting in nine different treatment groups. Drying was performed using a hot-air food dryer (LD-918BT, Liquip, Hwasung, Korea) with an air velocity of 2.5–3.0 m/s, and the dried samples were vacuum-packed and stored at room temperature (23°C–25°C) in a desiccator until hyperspectral image acquisition. After drying, 20 g of each sample was placed in a Petri dish (Ø 90 mm, 15 mm) for SWIR HSI. The samples were homogenized for 1 min using a grinder (A11 basic, IKA Werke, Staufen, Germany) after imaging. The samples were transported to a chemistry laboratory for proximate component analysis.

The camera used was a line-scan camera system (Headwall Photonics, Fitchburg, MA, USA) capable of capturing 275 wavelengths ranges of 894–2,504 nm (Fig. 1). Six tungsten-halogen lamps (100 W, 12 V, Light Bank; JCR 12V, Ushio, Tokyo, Japan) connected to fiber optics were used as light sources for imaging. The imaging sample was moved towards the camera using a DC motor-driven movable stage to obtain a hyperspectral image. The speed of the movable stage during the line scan was set at 3.48 mm/s, and the scan range was set to 600 scans/sample. The obtained hyperspectral image was in the form of a 3D hypercube with two spatial coordinates (x- and y-axes) and a wavelength range (λ) dimension, with a final size of 384 (x)×700 (y)×275 (λ). For data analysis, only the wavelength range of 1,000–2,350 nm was used to remove sensor noise, resulting in 232 wavelengths (Fig. 2).

Fig. 1. The SWIR hyperspectral sample images of black soldier fly larvae samples. SWIR, short-wave infrared.

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Fig. 2. Image acquisition step using SWIR hyperspectral imaging system. SWIR, short-wave infrared; ROI, region of interest.

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After the HSI process, the samples were ground for a period of 1 minute using a grinding mill (A11 basic, IKA Works). Proximate composition analysis was conducted by repeating the procedure three times, according to the AOAC method (AOAC, 2005). Moisture content was determined by drying the samples (1.0 g) at 105°C for 24 h. The moisture content was calculated using Eq. (1) after 24 h of drying.

The crude protein content was analyzed using the Kjeldahl method. Approximately 0.5 g of each sample was decomposed by adding a catalytic agent (1000 Kjeltabs S/3.5, Foss Tecator, Höganäs, Sweden) and 12 mL of H₂SO₄. The sample was heated at 420°C for 1 h and cooled. The nitrogen content was measured using a Kjeltec device (Kjeltec auto 2300 Analyzer, Foss Tecator), and the crude protein content was calculated by multiplying the nitrogen coefficient (4.76). Typically, a nitrogen coefficient of 6.25 is used for animal protein. However, there is a possibility of overestimating the crude protein content in insects owing to the presence of nitrogen in chitin. Therefore, recent studies have used a nitrogen coefficient of 4.76 to calculate the crude protein content (Cruz-Tirado et al., 2023; Janssen et al., 2017). The crude fat content was analyzed by ether extraction using a Soxhlet system. Crude fiber analysis was performed using filter bags (Ankom Technology, Macedon, NY, USA), and the difference between the weight of the insoluble residue when treated with 1.25% H₂SO₄ and 1.25% NaOH solution and the weight after painting was expressed as a percentage of the sample. The ash contents of the samples was analyzed using the combustion method. Approximately 2 g of each sample was heated by electric combustion for analysis. The sample was then placed in a 600°C electric furnace (CT-DMF2, Coretech, Ulsan, Korea) for 2 h. After cooling for 40 min in a desiccator, the sample was weighed to determine the amount of ash present by calculating the difference in weight before and after combustion.

A two-way ANOVA test was conducted to analyze the significant differences in the biochemical composition results of the sample according to the drying time and temperature, and the interaction p-value was calculated for both drying time and temperature. A one-way ANOVA test was conducted again for each drying time and temperature, and a post hoc analysis was performed using Duncan’s multiple range test for samples with significant differences (p<0.05). Basic statistics were obtained using the R statistical program (version 4.1.2, The R Foundation, Ames, IA, USA), with the CRAN mirror set to the USA (CA1) and ‘Agricolae’ libraries.

To mitigate the influence of external environmental factors, such as dark current noise and non-uniform lighting, spectral intensity calibration was conducted. For this purpose, white and dark references were acquired during image acquisition. The white reference was obtained using a white Teflon board (99.5% reflectance, 30 cm×30 cm×1 cm), whereas the dark reference was obtained by closing the camera lens cap and capturing an image with the light source turned off. The intensity calibration of the acquired hyperspectral image was performed using Eq. (2).

where $T_{ij}^R\left(\lambda \right)$ represents the spectrum of the sample at the pixel, $T_{ij}^D\left(\lambda \right)$ represents the spectrum value of the dark reference image, and $T_{ij}^W\left(\lambda \right)$ represents the spectrum of the white reference. The final X_c value represented a pixel-wise intensity-calibrated hyperspectral image, which is a relative intensity spectral image. Finally, the wavelength is extracted from the processed hyperspectral images.

The calibrated image was used to extract spectra by selecting the region of interest, and a masking image was created by setting the threshold value to 0.2 to select only the sample area. The masking image was then multiplied by all wavelength images to separate only the sample area of the spectrum (Fig. 2). The spectrum was extracted from all the pixels of the separated sample area and averaged to obtain the mean spectrum. Ten average spectra were extracted for each sample image, and 600 sample spectra were obtained and used for the subsequent multivariate analyses.

The acquired spectral data contain considerable noise. Many external factors, such as baseline correction, band shift, and light scattering, hinder the acquisition of pure data, and spectra preprocessing is usually performed for noise removal during the analysis. Normalization and deviation methods are commonly used for preprocessing. As there is no single best preprocessing technique, this study utilized Seven preprocessing methods to pre-process the acquired wavelengths. Three normalization methods (minimum, maximum, and range normalization), standard normal variate, multiplicative scatter correction (MSC), Savitzky-Golay 1^st derivation, and Savitzky-Golay 2^nd derivation were used in the spectral preprocessing process in this study.

PLS-R is a multivariate analysis method used to evaluate the correlation between various independent variables X and a dependent variable Y (Wold et al., 1984). PLS-R was used to predict the dependent variable Y using a regression equation. The PLS method used in this study is described by Eqs. (3) and (4). The PLS regression equation generates a regression model using the spectral data (X matrix, N samples×K wavelengths) and acquired parameter values as a reference (Y matrix, N samples×1).

In this context, Y is a matrix of dependent variables representing moisture, crude protein, crude fat, crude fiber, and crude ash content in the BSFL. X is an n×p matrix of independent variables corresponding to each spectral variable, where n is the number of spectra in the sample and p represents each wavelength range (nm). Matrix X is composed of a loading matrix P, a score matrix T, and an error matrix E. Matrix Y is composed of a loading matrix Q, a score matrix U, and an error matrix F. To develop a regression model, 70% of the 600 data points were randomly assigned to the calibration set, and the remaining 30% were assigned to the validation set during the spectrum analysis. Finally, 420 and 180 data points were included in the calibration and validation datasets, respectively.

In this study, root mean square error (RMSE) was used to calculate the model’s error rate (Lee et al., 2013). The formula for calculating RMSE is shown in Eq. (5).

Here, y_i,actual and y_i,predicted represent the actual reference values obtained through chemical experiments and the estimated predicted values from the developed PLS model, respectively. In addition, ‘n’ represents the number of actual samples. The model results were expressed as the coefficient of determination (R²), which was calculated using Eq. (6).

One advantage of HSI is its ability to generate chemical images of component distributions by simultaneously measuring spectral and spatial data (Faqeerzada et al., 2020). The beta coefficients obtained through the PLS-R analysis were used to generate a chemical image of the sample. In this process, the hyperspectral image was transformed into a 2D matrix, which was then multiplied by the PLS regression coefficients. The resulting 2D matrix was then transformed back into a 3D image, and the PLS chemical image was generated by summing the corresponding pixels of all band images. The chemical formula is given in Eq. (7):

where I_i represents the hypercube image measured at the ith wavelength band, R_i represents the beta coefficient values derived from the PLSR model, and C represents a constant. n denotes the number of wavelengths used in this study. All analyses and visualizations related to the wavelengths were performed using MATLAB 2021b (MathWorks, Natick, MA, USA). Fig. 3 shows the experimental flow.

Fig. 3. The algorithm development workflow for making black soldier fly larvae (BSFL) proximate content prediction model. SWIR, short-wave infrared; MSC, multiplicative scatter correction; SNV, standard normal variate; PLS-R, partial least-squares regression.

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Table 1 shows the proximate component analysis results for the dry matter (DM) of BSFL. The moisture content of the larvae decreased significantly with increasing drying time and temperature (p<0.05). The moisture content decreased the most at 70°C during drying. In this study, the larvae were dried at 70°C for 3 h, which was generally considered to be the end of the drying process, and the moisture content of the treatment group was found to be about 14.4%. Chia et al. (2020) reported that the moisture content of BSFL was around 9%–12%. The crude protein content of the larvae increased gradually with increasing drying time and temperature, and this increase was more significant at higher temperatures (p<0.05). In this study, the highest crude protein content (26.2%) was observed in the treatment group dried at 70°C for 3 h. Generally, the protein content of BSFL powder is reported to be approximately 30%–52.9% (Bessa et al., 2020), and Chia et al. (2020) reported that the protein content of BSFL was approximately 31.7% when fed agricultural byproducts. This study showed similar results to those of previous studies.

The crude fat content also increased from 30.1% to 46.2% with increasing drying time and temperature (p<0.05). As the moisture content decreased, the increasing trend in the proximate components in the samples led to an increase in the g/100 g protein and fat percentages, which in turn increased the total crude protein and fat content. In this study, the fat content was about 46.2% in the sample dried at 70°C for 3 h, which was considered the end of the drying process. Chia et al. (2020) reported that the fat content of BSFL varied depending on the feed (p<0.0001) and ranged from 9.5% to 49.0%. Caligiani et al. (2018) analyzed BSFL using the Soxhlet ethyl ether extraction method and reported a fat content of approximately 37.1%. Li et al. (2022) reported that fat content varied depending on the diet of the larvae. The crude fiber content (%) showed a gradual increase with increasing drying time at 60°C and 70°C, except for the 50°C dry treatment group. In this study, the crude fiber content increased significantly from 2.7% to 6.8% at the end of the drying process (p<0.05). Park et al. (2013) reported that the crude fiber contents of BSFL and pupae were 7.47% and 7.63%, respectively. In this study, the crude fiber content of BSFL significantly increased with increasing drying time and temperature (p<0.05) and reached 6.4% after drying for 3 h at 70°C. Park et al. (2013) reported that BSFL’s DM crude fiber content was about 9.41%, and Chia et al. (2020) reported a crude fiber content range of 6.7%–12.1%. In conclusion, as the drying time increased in this study, the moisture content decreased, and the amounts of crude protein, crude fat, crude fiber, and ash increased. Furthermore, the results were within a range similar to those reported in other studies.

Fig. 4 shows the SWIR hyperspectral spectral data of the BSFL. Each spectrum shows the average spectrum of the group according to drying temperature and drying time. It was confirmed that the wavelength intensity and pattern changed with drying time and temperature within a specific wave range. These results suggest the possibility of a proximate composition prediction using the wavelength of BSFL in the SWIR region. The average spectrum can be used to observe the overall spectrum pattern for each group by comparing the approximate spectral differences between the groups through spectrum intensity and shape differences. However, spectrum intensity can have a high SD owing to noise factors such as spectrum shifts, making it more practical to compare spectrum patterns rather than spectrum intensity (Park et al., 2021). In the case of Fig. 4, it is difficult to confirm the trends owing to spectrum shifts. Therefore, instead of comparing the spectral intensity using methods such as ANOVA, we aimed to build a proximate component prediction regression model for each group by conducting PLS-R analysis.

Fig. 4. The results of multiplicative scatter correction (MSC) preprocessed spectrum (1,000–2,350 nm) in black soldier fly larvae.

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Unlike spectrometers, hyperspectral images contain wavelength information for each pixel, making it possible to visualize information that is difficult to see with the naked eye. Therefore, in this study, chemical images were created for each component, including moisture, crude protein, crude fat, crude fiber, and crude ash content, and visualized according to their respective concentrations (Fig. 7). Red pixels represent high concentrations and dark blue pixels indicate low concentrations. As the drying time and temperature increased, the moisture content decreased gradually, which was monitored by observing an increasing number of blue pixels. For crude protein, crude fat, crude fiber, and crude ash, the number of red pixels increased with the drying time and temperature. It is confirmed that the proposed prediction model performs well.

Fig. 7. The chemical images of black soldier fly larvae.

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