Optimal Wavelength Combination Research Articles

A new framework for interval wavelength selection based on wavelength importance clustering

BackgroundWavelength selection is one of the key steps in spectral analysis and plays an irreplaceable role in improving model prediction accuracy and computational efficiency. High-dimensional spectral datasets contain substantial irrelevant information and redundant variables. Whereas, at current stage, such problem can be solved by existing abundant wavelength selection methods. However, it is difficult to achieve the balance between strong wavelength interpretability and prediction accuracy by those methods. As a result, there is an urgent need for a new method that can reach the point of balance. Resultswe propose a new framework for wavelength selection based on wavelength importance clustering (WIC) which attempts to establish a hierarchical relationship between wavelength points and attributions of response through a clustering algorithm, consequently, performing combinatorial and filtering to obtain the optimal wavelength combinations. In this paper, a new wavelength selection method (WIC-WRCKF) is constructed based on WIC, and four commonly used wavelength selection methods are selected to be compared with WIC-WRCKF. A large number of experiments are carried out on three publicly available datasets as well, namely, wheat, corn, and tablets. Compared with other methods, WIC-WRCKF has the highest prediction accuracy with high stability on the three datasets, and the number of wavelengths selected is small and highly interpretative, indicating that WIC-WRCKF has a better predictive ability. SignificanceThe wavelength selection method can significantly improve the model prediction accuracy, and the WIC architecture can effectively exploit the essence of the spectral data, which has great potential in the application of wavelength selection.

Methods to maximize detector count rates on small-angle neutron scattering diffractometers at reactor sources: I. Optimizing wavelength selection

Methods to determine the optimal neutron wavelength to maximize detector count rates on small angle neutron scattering (SANS) diffractometers at reactor sources is presented. Three experimental methods are used to determine the choice of optimal wavelength and collimation combination that maximizes the detector count rate. The wavelength optimization methods are applied to two different SANS diffractometers at the NCNR, and all methods are found to confirm the optimal wavelength of approximately 9.5 Å ± 0.5 Å for optically thin samples. The optimum wavelength is shifted by the wavelength-dependence of the sample transmission to 8.5 Å ± 0.5 Å for thicker absorbing samples or 6.5 Å ± 0.5 Å if the amount of multiple scattering needs to be minimized to limit scattering curve distortions. The same optimization methods can be applied to SANS diffractometers at other reactor facilities.

Discrimination of individual semiconducting and metallic single-walled carbon nanotubes by multi-wavelength photothermal microscopy

The coexistence of semiconducting and metallic single-walled carbon nanotubes (SWCNTs) after synthesis is one of the factors preventing their practical applications. A method for quantifying the purity of a nanotube sample with high accuracy is essential because each type has different applications. In this study, we investigated the accuracy in distinguishing between semiconducting and metallic SWCNTs using multi-wavelength photothermal microscopy. Linear discrimination analysis of the multi-wavelength signals of individual nanotubes was performed to show that the two types of SWCNTs could be distinguished with more than ∼95% accuracy when the SWCNTs were embedded in a dry polymer film. In contrast, the discrimination rate decreased to about 80% when the SWCNTs were dispersed in aqueous media. The optimal wavelength combinations, number of lasers required for discrimination, and relationship between the signal-to-noise ratio and discrimination rate are also discussed.

Hyperspectral estimation of maize (Zea mays L.) yield loss under lodging stress

The frequency and intensity of maize (Zea mays L.) yield disturbance caused by lodging stress are getting higher and higher, so it is of great significance to take effective methods to monitor the yield loss of lodging maize. This study aimed to explore the ability of hyperspectral technology to estimate maize yield loss under lodging. The lodging control experiment of maize was carried out, the changes of canopy hyperspectral and per unit yield loss of maize in multiple growth stages were analyzed. First, the successive projections algorithm (SPA) and recursive feature elimination (RFE) methods were used to select yield-sensitive wavelengths from original canopy spectrum (OCS) to achieve dimensionality reduction. Then, the fractional-order differential (FOD) transform was applied for the canopy spectrum, and the RFE method was used to select the optimal wavelength combination. Finally, the models of estimating per unit yield of maize with different lodging periods and days after lodging (DAL) were constructed by using the optimal wavelength combination, and the model was verified by leave-one-out cross-validation (LOOCV) method. It was found that the more serious the lodging severity, the greater the maize per unit yield loss. On 1, 7, 14, 21 DAL at vegetative tasseling (VT) stage and 1, 7 DAL at reproductive milk (R3) stage, the model accuracy of RFE method was 5.26%, 17.39%, 9.46%, 6.41%, 20.37% and 11.11% higher than that of SPA method. The estimation accuracy of lodging maize yield model was improved by using FOD method and RFE method (FOD-RFE). The R2 of the maize yield model of 1.4-order, 0.9-order, 1.8-order, 0.6-order on 1, 7, 14 and 21 DAL at VT stage reached 0.90, 0.89, 0.92, 0.93, which were 12.5%, 11.11%, 13.58% and 12.05% higher than OCS-RFE. The R2 of the maize yield model of 1.5-order and 1.5-order on 1 and 7 DAL at R3 stage were 0.84 and 0.87, which were 29.23% and 24.29% higher than OCS-RFE. Therefore, fractional-order differential transform and spectral feature selection could improve the estimation accuracy of maize yield under lodging stress. The hyperspectral technology could be used to quickly estimate maize yield loss under lodging stress.

Development and application of an optimal three-wavelength combination for liquid film measurement with absorption spectroscopy

In the simultaneous measurement of liquid film temperature and thickness based on multi-wavelength absorption spectroscopy, selecting optimal wavelength combinations can significantly improve the measurement accuracy. In the work, the absorption spectra of e-liquid at different temperatures were measured firstly. And ten sets of two-wavelength and three-wavelength combinations were then established based on five specific wavelengths from the absorption spectra, respectively. And the measurement accuracy of all the combinations were validated with a home-made calibration system. Among them, an optimal three-wavelength combination were selected. Finally, the evaporation processes of e-liquid films at three initial thicknesses (921/780/629 μm) on a horizontal quartz plate were then investigated with the optimal combination. The variation trends of film temperature and thickness measured by the combination were consistent with imaging method and thermocouple. It was found that the optimal three-wavelength combination could achieve high accuracy in simultaneous measurement of liquid film temperature and thickness.

A non-contact oxygen saturation detection method based on dynamic spectrum

Blood oxygen saturation (SpO2) is an important monitoring indicator for many respiratory diseases. Non-contact oximetry offers outstanding advantages in both coronavirus pandemic monitoring and sleep monitoring, but at the same time poses both challenges regarding technology and environment. Therefore, we propose a method for non-contact SpO2 measurement based on the principle of DS (dynamic spectrum) in this paper. A multispectral camera with 24 wavelengths (range in 660 nm-950 nm) is used to capture video of the people's cheek region, and then the two-dimensional images are converted into a one-dimensional temporal PPG signal. After pre-processing the PPG signal, the 24 wavelengths DS values are extracted. The optimal wavelength combination is obtained by wavelength screening using the one-by-one elimination method, and a PLS (partial least squares) model is established using the SpO2 values measured simultaneously by pulse oximetry as the modeled true values. The facial videos of eight healthy subjects were collected, and a total of 140 valid samples were obtained. By analyzing the modeling results, the regression coefficient (R) and root mean square error (RMSE) of the modeled set were 0.6366 and 0.9906, respectively. This method can significantly respond to the variation of SpO2, and the prediction results are approaching to the prediction accuracy (±2%) of most pulse oximeters in the market. Using DS theory in this method eliminates in principle the interference of static tissue, individual differences, and environment. It fully meets the strong demand for non-contact oximetry and provides a new measurement idea.

Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index

Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters.

Wavelength and frequency optimization in spatial frequency domain imaging for two-layer tissue

Spatial frequency domain imaging is a non-contact, wide-field, fast-diffusion optical imaging technique, which in principle uses steady-state spatially modulated light to irradiate biological tissue, reconstruct two-dimensional or three-dimensional tissue optical characteristic map through optical transmission model, and further quantify the spatial distribution of tissue physiological parameters by multispectral imaging technique. The selection of light source wavelength and light field spatial modulation frequency is directly related to the accuracy of tissue optical properties and tissue physiological parameters extraction. For improvement of the measurement accuracy of optical properties and physiological parameters in the two-layer tissue, a multispectral spatial frequency domain imaging system is built based on liquid crystal tunable filter, and a data mapping table of spatially resolved diffuse reflectance and optical properties of two-layer tissue is established based on scaling Monte Carlo method. Combined with the dispersion effect and window effect of light-tissue interaction, the study applies numerical simulation to optimize the wavelength in the 650-850 nm range with spectral resolution of 10 nm. In order to minimize the uncertainty of the optical properties, Cramér-Rao bound is used to optimize the optical field spatial modulation frequency by transmitting the uncertainty of optical properties. The results showed that in order to realize the detection of melanin, oxyhemoglobin, deoxyhemoglobin, water and other physiological parameters in two-layer tissue, the best wavelength combination was determined as 720, 730, 760 and 810 nm according to the condition number. The findings of the Cramér-Rao bound analysis reveal that the uncertainty of optical characteristics for the frequency combinations [0, 0.3] mm-1, [0, 0.2] mm-1, and [0, 0.1] mm-1 increases successively. Under the optimal combination of wavelength and frequency, the diffuse reflectance of the gradient gray-scale plate measured by the multi-spectral spatial frequency domain imaging system is linearly correlated with the calibration value. The error between the measured liquid phantom absorption coefficient and the collimation projection system based on colorimetric dish is less than 2%. The experimental results of human brachial artery occlusion indicate that under the optimal wavelength combination, the change of the second layer absorption coefficient captured by the three frequency combinations decreases in turn, so as the change of oxygen saturation.

A novel method for selecting the set optimal wavelength combination in multi-spectral transmission image

In the process of detecting heterogeneity in breast tissue based on multi-spectral transmission imaging, the detection accuracy will be affected due to the high redundancy degree of information between bands. In order to select the reasonable wavelength combination, this paper uses various nonlinear transformations to convert the multi-spectral images into spectral data for the first time, so as to select the set optimal wavelength combination based on the successive projections algorithm (SPA). Firstly, we design the collection experiment of 4-wavelength multi-spectral image. And then, K-SVD dictionary learning method, texture extraction method and gray correlation analysis method are used to obtain the feature spectral information. Finally, the set optimal wavelength combination is selected based on SPA. The experimental results show that random forest (RF) classification model and Faster-RCNN recognition models effectively verify that the combination of wavelengths 1,2,4 selected has the highest accuracy in the heterogeneous detection. In conclusion, this paper uses modulation-frame accumulation technique to improve the quality of multi-spectral transmission images. And based on the RF and Faster-RCNN models, the effectiveness of SPA-based optimal wavelength combination method proposed is verified, which will provide a new idea of feature wavelength selection for screening early breast masses through multi-spectral transmission imaging.

Development of simplified models for nondestructive testing of rice (with husk) protein content using hyperspectral imaging technology

The study aimed to establish predictive models of protein content in rice (with husk) using a hyperspectral imaging (HSI) system in a collection of 87 rice varieties in China in the wavelength range of 938–2215 nm and the first established multivariate calibration models over the full wavelength range by using partial least-square regression (PLSR), principal component regression (PCR) and least-square support vector regression (LS-SVR). In predictive model optimisation, the optimal wavelengths were selected by using regression coefficients (RC) as discriminating factors to establish PLSR models. According to the RC of models, the optimal wavelength combination was 17 and 7 characteristics. The model based on the 17-characteristic wavelengths was determined as the optimal optimisation model with coefficients of determination for prediction of 0.8011 and root mean square error of prediction of 0.52. The mapping of protein content was achieved by transferring a quantitative model to each pixel. According to the visualisation image, the distribution of rice protein could be understood, thus realising the possibility of on-line detection of protein content by using HSI technology.

Optimal partner wavelength combination method applied to NIR spectroscopic analysis of human serum globulin

Human serum globulin (GLB), which contains various antibodies in healthy human serum, is of great significance for clinical trials and disease diagnosis. In this study, the GLB in human serum was rapidly analyzed by near infrared (NIR) spectroscopy without chemical reagents. Optimal partner wavelength combination (OPWC) method was employed for selecting discrete information wavelength. For the OPWC, the redundant wavelengths were removed by repeated projection iteration based on binary linear regression, and the result converged to stable number of wavelengths. By the way, the convergence of algorithm was proved theoretically. Moving window partial least squares (MW-PLS) and Monte Carlo uninformative variable elimination PLS (MC-UVE-PLS) methods, which are two well-performed wavelength selection methods, were also performed for comparison. The optimal models were obtained by the three methods, and the corresponding root-mean-square error of cross validation and correlation coefficient of prediction (SECV, RP,CV) were 0.813 g L−1 and 0.978 with OPWC combined with PLS (OPWC-PLS), and 0.804 g L−1 and 0.979 with MW-PLS, and 1.153 g L−1 and 0.948 with MC-UVE-PLS, respectively. The OPWC-PLS and MW-PLS methods achieved almost the same good results. However, the OPWC only contained 28 wavelengths, so it had obvious lower model complexity. Thus it can be seen that the OPWC-PLS has great prediction performance for GLB and its algorithm is convergent and rapid. The results provide important technical support for the rapid detection of serum.

Comparative study of partial least squares and neural network models of near-infrared spectroscopy for aging condition assessment of insulating paper

Near-infrared spectroscopy (NIRS) is a rapid and non-destructive detection method for component determination and quantitative analysis with broad applications in numerous fields. In recent years, NIRS has started to be used in the aging condition assessment of power transformers. However, the real applications of NIRS are constrained by the lack of evaluation database and accurate prediction algorithms. In this paper, we aim at comparing different NIRS modeling methods and improving diagnostic accuracy. We build the evaluation database via the preparation of 230 specimens derived from three typical types of insulating paper. Calibration models are established by linear method-partial least squares (PLS) and nonlinear method-back propagation neural network (BPNN) to map the relationship between spectra and the degree of polymerization (DP). The DP prediction results show that using full NIR spectra as the input of the PLS model does not ensure a high prediction accuracy, and it is improved by competitive adaptive reweighted sampling (CARS) that selects the optimal wavelength combinations. Prediction precisions given by BPNN and CARS-BPNN models are shown to be less satisfactory than that of CARS-PLS. We process the original spectra with principal component analysis (PCA) as the input of BPNN and the PCA-BPNN model realizes high prediction precision for three types of paper (RMSE ⩽ 24, r = 0.99). With the identification of paper type by the k-nearest neighbors (KNN) method before prediction, the KNN-PCA-BPNN model solves the problem of the low prediction precision for mixed (unknown) paper samples (RMSE = 36, r = 0.98), which facilitates future field tests as well as related applications in practice.

The determination of plasma voriconazole concentration by surface-enhanced Raman spectroscopy combining chemometrics

In this study, a simple detection method was constructed using gold nanoparticles as a substrate in surface-enhanced Raman scattering that combined chemometrics to measure plasma voriconazole concentration. In this application, an adaptive, iteratively reweighted, penalized least squares (air-PLS) method was utilized to deduct matrix background and correct baseline spectral drift. A moving window partial least squares (MWPLS) method was used to select the optimal wavelength combination to construct a concentration prediction model. Evaluation of the results demonstrated that the final model had an excellent capability for prediction of plasma voriconazole concentration, applicable over the concentration range 3 × 10−3–200 μg/mL with a correlation coefficient of above 0.99. In conclusion, using surface enhanced Raman spectroscopy with chemometrics, we propose a novel and efficient approach that plasma voriconazole concentration can be measured in tens of seconds with a simple pretreatment.

Nondestructive Assessment of Egg Freshness using a Synchronous Fluorescence Spectral Technique

Freshness is an important index of egg quality. In this study, a synchronous fluorescence spectral technique was employed to determine the freshness of an intact egg. Synchronous fluorescence spectra of intact eggs were acquired using a fluorescence spectrometer supported by a laboratory fluorescence acquisition device and egg freshness (Haugh Unit) was obtained using destructive methods. Eggs feature fluorescence signals were mainly concentrated in two regions: A (excitation wavelength of 290 nm over the emission wavelength range of 320-380 nm) and B (excitation wavelength range of 380-570 nm over the emission wavelength range of 610-735 nm). The two regions were selected as regions of interest, which include 2581 Excitation-Emission (Ex-Em) wavelengths; stepwise discrimination analysis was performed on the 2581 Ex-Em wavelengths to choose optimal Ex-Em wavelength combinations. A Multiple Linear Regression (MLR) prediction model was built using fluorescence signals based on the optimal Ex-Em wavelength combinations. The results revealed that the freshness of an egg could be accurately predicted with Rp2 of 0.8879 and a root mean square error estimated by validation (SEP) of 6.2896. This work demonstrates that the synchronous fluorescence spectral technique has high potential for nondestructive sensing of egg freshness.

Ageing condition assessment of oil‐paper insulation using near infrared spectroscopy detection and analytical technique

Near Infrared Spectroscopy (NIRS), as one of analysis technologies, has shown promisingly industrial applications for significant properties for recent decades such as fast response, preciseness, non-intrusion etc. Here, the authors employed NIRS coupled with a series of physical and chemical tests to assess the ageing condition of oil-paper insulation, which is responsible for the main insulation type of high-voltage power transformer. Among these procedures, the data analytic algorithms are of utmost importance to determine the evaluation precision. After plenty of trial-and-error, Savitzky-Golay (S-G) convolution was finally utilised to de-noise samples and improve the spectral data quality. The competitive adaptive reweighted sampling (CARS) was used to select the optimal wavelength combination of NIRS, which is found able to fully extract the effectively spectral information and reduce dimensions of spectral data. Based on the above-mentioned techniques, the quantitative analysis model of NIRS was established by partial least squares (PLS), which could synthetically process the spectral data and the degree of polymerisation (DP) of paper samples. The results indicated that compared with the traditional detection methods, the NIRS analysis is a powerful and informative tool to characterise the condition of oil-paper insulation without intrusion or damage to transformers.

Band Selection Method of Absorption Peak Perturbance for the FTIR/ATR Spectrum Analysis

The rapid quantification method of human serum glucose was established by using the Fourier transform infrared spectroscopy (FTIR) and attenuated total reflection (ATR). By the subtracted spectra between glucose aqueous solution and de-ionized water, absorption peaks are calculated in fingerprint area. Based on these absorption peaks and multiple linear regression (MLR) model, discrete band selection method of absorption peaks disturbance model (APDM) was developed. 5 absorption peaks 1150 cm-1, 1103 cm-1, 1078 cm-1, 1034 cm-1, 991 cm-1 were found in fingerprint area. Used these absorption peaks to establish absorption peaks disturbance model, the optimal wavelength combinations are 1140 cm-1, 1096 cm-1, 1084 cm-1, 1030 cm-1, 993 cm-1, the corresponding C-RMSEP and C-RP are 1.164 mmol/L and 0.828 respectively. The results show that the optimal prediction effect of APDM was obviously better than the one of the Partial least squares (PLS) model, and the complexity of the optimal model is reduced greatly also. The results also provide a theoretical basis for design of small and portable human serum glucose spectrometer.

Combination of heuristic optimal partner bands for variable selection in near‐infrared spectral analysis

AbstractVariable selection plays a critical role in the analysis of near‐infrared (NIR) spectra. A method for variable selection based on the principle of the successive projection algorithm (SPA) and optimal partner wavelength combination (OPWC) was proposed for NIR spectral analysis. The method determines a number of knot variables with sufficient independence by SPA, and candidate variable bands with a definite width are defined. The cooperative effect of the bands is then evaluated with the partial least squares regression model by using the method of OPWC. The performance of the proposed method was compared with those of SPA, OPWC, randomization test, competitive adaptive reweighted sampling, and Monte Carlo uninformative variable elimination by using NIR datasets for pharmaceutical tablets, corn, and soil. The results show that the proposed method can select informative variable bands with a cooperative effect and improves the model for quantitative analysis.

Optimal partner wavelength combination method with application to near-infrared spectroscopic analysis

Using binary linear regression, the optimal partner wavelength of each wavelength is selected in an initial wavelength screening region. On the basis of strategy above, a novel approach for selecting appropriate wavelengths combination, named optimal partner wavelength combination (OPWC) coupled with partial least squares (PLS), is proposed, and was successfully applied for reagent-free near-infrared spectroscopic analysis of organic matter in soil. Moving window PLS (MW-PLS), successive projections algorithm (SPA) and Monte Carlo uninformative variable elimination (MC-UVE), which are well-performed wavelength selection methods, were also conducted for comparison.The OPWC-PLS, MW-PLS, SPA-PLS and MC-UVE-PLS methods selected 14, 210, 63, 199 wavelengths, respectively. The root-mean-square error and correlation coefficients for leave-one-out cross validation were 0.165gkg−1 and 0.967 for OPWC-PLS, 0.163gkg−1 and 0.968 for MW-PLS, 0.198gkg−1 and 0.953 for SPA-PLS, and 0.190gkg−1 and 0.956 for MC-UVE-PLS, respectively. The results indicate that OPWC-PLS and MW-PLS methods were almost the same, and were obvious better than SPA-PLS and MC-UVE-PLS methods. But the OPWC only contained 14 wavelengths, which is a high efficient approach for extracting information wavelengths and mitigating redundant wavelengths. OPWC can be also provided valuable reference for designing small dedicated spectrometers with a high signal-to-noise ratio.OPWC can be programmed determined, which has small amount of calculation and high operating speed, and it is a deterministic search technique whose results are reproducible. We believe that OPWC has such applicability and can be applied to other fields of spectroscopic analysis.

A system for simultaneous near-infrared reflectance and transillumination imaging of occlusal carious lesions.

Clinicians need technologies to improve the diagnosis of questionable occlusal carious lesions (QOC's) and determine if decay has penetrated to the underlying dentin. Assessing lesion depth from near-infrared (NIR) images holds great potential due to the high transparency of enamel and stain to NIR light at λ=1300-1700-nm, which allows direct visualization and quantified measurements of enamel demineralization. Unfortunately, NIR reflectance measurements alone are limited in utility for approximating occlusal lesion depth >200-μm due to light attenuation from the lesion body. Previous studies sought to combine NIR reflectance and transillumination measurements taken at λ=1300-nm in order to estimate QOC depth and severity. The objective of this study was to quantify the change in lesion contrast and size measured from multispectral NIR reflectance and transillumination images of natural occlusal carious lesions with increasing lesion depth and severity in order to determine the optimal multimodal wavelength combinations for estimating QOC depth. Extracted teeth with varying amounts of natural occlusal decay were measured using a multispectral-multimodal NIR imaging system at prominent wavelengths within the λ=1300-1700-nm spectral region. Image analysis software was used to calculate lesion contrast and area values between sound and carious enamel regions.

Optimal wavelengths for near-infrared multispectral imaging of atherosclerotic plaque

Near-infrared multispectral imaging (NIR-MSI) is a potentially effective technique for evaluation of atherosclerotic plaque. In the present study, the optimal wavelength combinations in the NIR wavelength range for detecting plaque were investigated. Atherosclerotic phantoms were observed using a NIR camera at different wavelengths in the range 1150–1790 nm. It was found that the plaque phantom could be detected using just three suitably chosen wavelengths in the neighborhood of absorption peaks due to the lipid at 1210 and 1730 nm. Although higher quality images could be obtained using the peak at 1730 nm due to the stronger absorption at this wavelength, the peak at 1210 nm offered the advantage of a larger optical penetration depth.