Spatial-frequency domain imaging coupled with frequency optimization for estimating optical properties of two-layered food and agricultural products

Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review

Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper.

Applications of optical property measurement for quality evaluation of agri-food products: a review

Spectroscopic techniques coupled with chemometric approaches have been widely used for quality evaluation of agricultural and food (agri-food) products due to the nondestructive, simple, fast, and easy characters. However, these techniques face the issues or challenges of relatively weak robustness, generalizability, and applicability in modeling and prediction because they measure the aggregate amount of light interaction with tissues, resulting in the combined effect of absorption and scattering of photons. Optical property measurement could separate absorption from scattering, providing new insights into more reliable prediction performance in quality evaluation, which is attracting increasing attention. In this review, a brief overview of the currently popular measurement techniques, in terms of light transfer principles and data analysis algorithms, is first presented. Then, the emphases are put on the recent advances of these techniques for measuring optical properties of agri-food products since 2000. Corresponding applications on qualitative and quantitative analyses of quality evaluation, as well as light transfer simulations within tissues, were reviewed. Furthermore, the leading groups working on optical property measurement worldwide are highlighted, which is the first summary to the best of our knowledge. Finally, challenges for optical property measurement are discussed, and some viewpoints on future research directions are also given.

Analysis of Light Penetration Depth in Apple Tissues by Depth-Resolved Spatial-Frequency Domain Imaging

Spatial-frequency domain imaging (SFDI) has been developed as an emerging modality for detecting early-stage bruises of fruits, such as apples, due to its unique advantage of a depth-resolved imaging feature. This paper presents theoretical and experimental analyses to determine the light penetration depth in apple tissues under spatially modulated illumination. Simulation and practical experiments were then carried out to explore the maximum light penetration depths in 'Golden Delicious' apples. Then, apple experiments for early-stage bruise detection using the estimated reduced scattering coefficient mapping were conducted to validate the results of light penetration depths. The results showed that the simulations produced comparable or a little larger light penetration depth in apple tissues (~2.2 mm) than the practical experiment (~1.8 mm or ~2.3 mm). Apple peel further decreased the light penetration depth due to the high absorption properties of pigment contents. Apple bruises located beneath the surface peel with the depth of about 0-1.2 mm could be effectively detected by the SFDI technique. This study, to our knowledge, made the first effort to investigate the light penetration depth in apple tissues by SFDI, which would provide useful information for enhanced detection of early-stage apple bruising by selecting the appropriate spatial frequency.

Study on Sustainable Agricultural Structure Optimization Method Based on Multiobjective Optimization Algorithm

Agricultural sustainable development is one of the themes of human and nature harmonious coexistence. Adjusting and optimizing agricultural structure are an important direction to improve the level of agricultural sustainable development. In this paper, related research status of the sustainable development of agriculture is analyzed; it shows that there is lack of scientific theories guidance for agricultural sustainable development. In order to optimize sustainable development of agriculture industry structure, the guidance theory of and its optimization are studied. Based on multiobjective optimization theory, several key factors that affect agricultural sustainable development and the main target indexes of agricultural sustainable development are analyzed, the mathematical model of the evaluation of the sustainable development of agriculture is established, and the solution to optimize the multiobjective model is studied. Finally, the agricultural industry sustainable development in a certain area is taken as the research object in this paper; the mathematical model and solving method of agricultural sustainable development evaluation are studied; it provides a guidance to optimize the regional agricultural industrial structure and improve the quality of agricultural sustainable development.

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.

Optical Property Mapping of Apples and the Relationship With Quality Properties

This paper reports on the measurement of optical property mapping of apples at the wavelengths of 460, 527, 630, and 710 nm using spatial-frequency domain imaging (SFDI) technique, for assessing the soluble solid content (SSC), firmness, and color parameters. A laboratory-based multispectral SFDI system was developed for acquiring SFDI of 140 "Golden Delicious" apples, from which absorption coefficient (μ _a ) and reduced scattering coefficient (μ _s ') mappings were quantitatively determined using the three-phase demodulation coupled with curve-fitting method. There was no noticeable spatial variation in the optical property mapping based on the resulting effect of different sizes of the region of interest (ROI) on the average optical properties. Support vector machine (SVM), multiple linear regression (MLR), and partial least square (PLS) models were developed based on μ _a , μ _s ' and their combinations (μ _a × μ _s ' and μ _eff ) for predicting apple qualities, among which SVM outperformed the best. Better prediction results for quality parameters based on the μ _a were observed than those based on the μ _s ', and the combinations further improved the prediction performance, compared to the individual μ _a or μ _s '. The best prediction models for SSC and firmness parameters [slope, flesh firmness (FF), and maximum force (Max.F)] were achieved based on the μ _a × μ _s ', whereas those for color parameters of b* and C* were based on the μ _eff , with the correlation coefficients of prediction as 0.66, 0.68, 0.73, 0.79, 0.86, and 0.86, respectively.

Tutorial on the Use of Deep Learning in Diffuse Optical Tomography

Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography.

Spatial Frequency Domain Imaging System Calibration, Correction and Application for Pear Surface Damage Detection

Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique for optical property detection. This study aimed to establish an SFDI system and investigate the effects of system calibration, error analysis and correction on the measurement of optical properties. Optical parameter characteristic measurements of normal pears with three different damage types were performed using the calibrated system. The obtained absorption coefficient μ_a and the reduced scattering coefficient μ'_s were used for discriminating pears with different surface damage using a linear discriminant analysis model. The results showed that at 527 nm and 675 nm, the pears' quadruple classification (normal, bruised, scratched and abraded) accuracy using the SFDI technique was 92.5% and 83.8%, respectively, which has an advantage compared with the conventional planar light classification results of 82.5% and 77.5%. The three-way classification (normal, minor damage and serious damage) SFDI technique was as high as 100% and 98.8% at 527 nm and 675 nm, respectively, while the classification accuracy of conventional planar light was 93.8% and 93.8%, respectively. The results of this study indicated that SFDI has the potential to detect different damage types in fruit and that the SFDI technique has a promising future in agricultural product quality inspection in further research.

Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor. In this research, the mismatches between measurements and simulations were explored by studying the influences from light source and the incident and detection angle on the diffuse reflectance profiles. After reducing the mismatches caused by the factors mentioned above, the simulated diffuse reflectance profiles matched well with the measurements, with R2 values above 0.99. Successively, metamodels linking the optical properties with the diffuse reflectance profiles were respectively built based on the measured and simulated profiles. The prediction performance of these metamodels was comparable, both obtaining R2 values above 0.96. Proper correction for these sources of mismatches between measurements and simulations thus allows to build a simulation-based metamodel with a wide range of desired optical properties that is applicable to different measurement configurations.

Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues

Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. This review first describes the typical SFDI system and the principle for estimating optical properties using the SFDI technique. Then, the applications of SFDI in the fields of biomedicine, as well as food and agriculture, are reviewed, including burn assessment, skin tissue evaluation, tumor tissue detection, brain tissue monitoring, and quality evaluation of agro-products. Finally, a discussion on the challenges and future perspectives of SFDI for optical property estimation is presented.