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A decomposition Mueller matrix method is proposed for detection of miRNA and enhanced by using a surface plasmon resonance (SPR). In the proposed approach, a Mueller matrix decomposition method is employed to extract the linear birefringence (LB) and circular dichroism (CD) properties of the miRNA sample. The accuracy of the LB and CD measurements is enhanced through the use of a high-resolution antimonene-based SPR prism coupler with DNA-linked gold nanoparticles (AuNPs). The feasibility of the proposed method is demonstrated by measuring the LB orientation angle (α) and CD property (R) of two miRNA aqueous solutions (hsa-miR-125-5p and hsa-miR-21-5p) over the concentration range of 0∼1000 fM in both cases. The results show that, for both samples, α and R vary linearly with the change in the miRNA concentration. Furthermore, the values of α and R obtained for the two samples are quantifiably different, and hence the selectivity of the proposed SPR sensor is confirmed. Overall, the results highlight the potential of the proposed sensor as a valuable tool for miRNA detection with prospective applications in cancer diagnosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Polarimetric imaging systems combining machine learning is emerging as a promising tool for the support of diagnosis and intervention decision-making processes in cancer detection/staging. A present study proposes a novel method based on Mueller matrix imaging combining optical parameters and machine learning models for classifying the progression of skin cancer based on the identification of three different types of mice skin tissues: healthy, papilloma, and squamous cell carcinoma. Three different machine learning algorithms (K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM)) are used to construct a classification model using a dataset consisting of Mueller matrix images and optical properties extracted from the tissue samples. The experimental results show that the SVM model is robust to discriminate among three classes in the training stage and achieves an accuracy of 94 % on the testing dataset. Overall, it is provided that polarimetric imaging systems and machine learning algorithms can dynamically combine for the reliable diagnosis of skin cancer., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

The farming industry is facing the major challenge of intensive and inefficient harvesting labors. Thus, an efficient and automated fruit harvesting system is required. In this study, three object classification models based on Yolov5m integrated with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs), respectively, are proposed for the automatic detection of tomato fruit. The various models were trained using 1508 normalized images containing three classes of cherry tomatoes, namely ripe, immature, and damaged. The detection accuracy for the three classes was found to be 94%, 95%, and 96%, respectively, for the modified Yolov5m + BoTNet model. The model thus appeared to provide a promising basis for the further development of automated harvesting systems for tomato fruit.

Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.

Significance: Nonenzymatic glycation of collagen covalently attaches an addition of sugar molecules that initially were involved in a reversibly reaction with amino groups on the protein. Due to the ultimate formation of stable irreversible advanced glycation end products, the process of glycation leads to abnormal irreversible cross-linking, which ultimately accumulates with age and/or diabetes in the extracellular matrix, altering its organization., Aim: We report the use of dual-retarder Mueller polarimetry in conjunction with phase retardance to differentiate collagen cross-linking in a normal collagen gel matrix from that in tissues with nonenzymatic cross-linking., Approach: A dual-liquid crystal-based Mueller polarimetry system that involves electronic modulation of polarization state generators (PSGs) was employed to produce all types of polarization states without moving any part and enable detection of the signal directly using a Stokes polarimeter. The linear phase retardance response was obtained for the characterization of the solution and gel forms of collagen using differential Mueller matrix analysis., Results: We found that linear phase retardance measurements via differential Mueller matrix polarimetry successfully differentiated collagen gel matrices with different degrees of cross-linking formed by a nonenzymatic glycation process and demonstrated that this technology constitutes a quick and simple modality., Conclusions: This approach has high sensitivity for studying differences in fibrillar cross-linking in glycated collagen. Further, our work suggests that this method of structural analysis has potential clinical diagnostic value owing to its noninvasive and cost-efficient nature., (© 2022 The Authors.)

Significance: The combination of polarized imaging with artificial intelligence (AI) technology has provided a powerful tool for performing an objective and precise diagnosis in medicine., Aim: An approach is proposed for the detection of hepatitis B (HB) virus using a combined Mueller matrix imaging technique and deep learning method., Approach: In the proposed approach, Mueller matrix imaging polarimetry is applied to obtain 4 × 4 Mueller matrix images of 138 HBsAg-containing (positive) serum samples and 136 HBsAg-free (negative) serum samples. The kernel estimation density results show that, of the 16 Mueller matrix elements, elements M 22 and M 33 provide the best discriminatory power between the positive and negative samples., Results: As a result, M 22 and M 33 are taken as the inputs to five different deep learning models: Xception, VGG16, VGG19, ResNet 50, and ResNet150. It is shown that the optimal classification accuracy (94.5%) is obtained using the VGG19 model with element M 22 as the input., Conclusions: Overall, the results confirm that the proposed hybrid Mueller matrix imaging and AI framework provides a simple and effective approach for HB virus detection., (© 2022 The Authors.)

A graphene-based surface plasmon resonance (SPR) prism coupler sensor is proposed for the rapid detection of immunoglobulin G (IgG) antibodies. The feasibility of the proposed sensor is demonstrated by measuring the IgG concentration in phantom mouse and human serum solutions over the range of 0-250 ng/mL. The results show that the circular dichroism and principal fast axis angle of linear birefringence increase in line with increases in IgG concentration over the considered range. Moreover, the proposed device has a resolution of 5-10 ng/mL and a response time of less than three minutes. In general, the sensor provides a promising approach for IgG detection and has significant potential for rapid infectious viral disease testing applications.

Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application., Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm., Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors., Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier., Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

A dual liquid-crystal variable retarder Mueller polarimetry system incorporating a gold-based surface plasmon resonance prism coupler was proposed for extracting the optical properties of serum albumin protein media in the reflectance configuration. The feasibility of the proposed system was demonstrated by measuring the circular dichroism and circular birefringence properties of glucose tissue phantom solutions with different albumin concentrations. The results showed that the circular dichroism increased with albumin concentration, while the optical rotation angle increased with glucose concentration. Both properties reduced over time as a result of the protein glycation effect, which led to a gradual reduction in the glucose content of the sample.

The strawberry ( Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30-40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases-leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

A surface plasmon resonance (SPR) prism coupler is proposed for the high-resolution non-invasive (NI) measurement of the circular birefringence (CB) properties of turbid media. The feasibility of the proposed device is demonstrated by means of numerical simulations. It is shown that the SPR sensor enables the CB properties to be detected with a resolution of up to 8.9 × 10 -7 RIU (refractive index units) for refractive indices in the range of 1.3∼1.4. Moreover, for tissue phantom solutions containing 2% lipofundin, the device has a detection limit of 3.72 mg/dL. This resolution performance satisfies the detection limit of 10 mg/dL stipulated by the U.S FDA for point-of-care glucose monitoring devices. Thus, the proposed SPR sensor has significant potential for NI glucose sensing in such applications as diabetes detection and management.

A decoupled analytical technique based on the Stokes-Mueller matrix decomposition method extracts polarization properties of human blood plasma, collagen solution, and calfskin. The proposed method is applied initially to extract the nine effective optical parameters of human blood plasma samples containing D-glucose powder with concentrations ranging from 0-1 M. The optical rotation angle of circular birefringence (CB) increases linearly with the glucose concentration in human blood plasma samples (r 2 =0.9782) and in tissue phantom samples (r 2 =0.9939). Meanwhile, the phase retardance of linear birefringence (LB) increases slightly from 0° to almost 2° as the D-glucose concentration increases. However, for the plasma samples, the optical rotation angle increases by 1.07±0.1  deg for each additional mole of D-glucose, while, for the tissue phantom samples, the optical rotation angle increases by 0.75±0.1  deg. For collagen solutions with concentrations ranging from 0 to 2 mg/mL, a strong linear relationship (r 2 =0.9936) is observed between the phase retardance of linear birefringence and the collagen concentration. Finally, for the calfskin samples, the linear birefringence reduces exponentially (r 2 =0.9689) over time following collagenase treatment. Overall, the decoupled analytical model provides a reliable and straightforward technique for detecting the optical properties of laboratory and natural biological samples. As a result, it has significant potential for diagnostic applications and the structural analysis of biological tissues.

A method is proposed for determining the glucose concentration on the human fingertip by extracting two optical parameters, namely the optical rotation angle and the depolarization index, using a Mueller optical coherence tomography technique and a genetic algorithm. The feasibility of the proposed method is demonstrated by measuring the optical rotation angle and depolarization index of aqueous glucose solutions with low and high scattering, respectively. It is shown that for both solutions, the optical rotation angle and depolarization index vary approximately linearly with the glucose concentration. As a result, the ability of the proposed method to obtain the glucose concentration by means of just two optical parameters is confirmed. The practical applicability of the proposed technique is demonstrated by measuring the optical rotation angle and depolarization index on the human fingertip of healthy volunteers under various glucose conditions., ((2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).)

A differential Mueller matrix polarimetry technique is proposed for obtaining non-invasive (NI) measurements of the glucose concentration on the human fingertip. The feasibility of the proposed method is demonstrated by detecting the optical rotation angle and depolarization index of tissue phantom samples containing de-ionized water (DI), glucose solutions with concentrations ranging from 0~500 mg/dL and 2% lipofundin. The results show that the extracted optical rotation angle increases linearly with an increasing glucose concentration, while the depolarization index decreases. The practical applicability of the proposed method is demonstrated by measuring the optical rotation angle and depolarization index properties of the human fingertips of healthy volunteers.

A surface plasmon resonance (SPR)-enhanced method is proposed for measuring the circular dichroism (CD), circular birefringence (CB), and degree of polarization (DOP) of turbid media using a Stokes–Mueller matrix polarimetry technique. The validity of the analytical model is confirmed by means of numerical simulations. The simulation results show that the proposed detection method enables the CD and CB properties to be measured with a resolution of 10 ? 4 refractive index unit (RIU) and 10 ? 5 ?? RIU , respectively, for refractive indices in the range of 1.3 to 1.4. The practical feasibility of the proposed method is demonstrated by detecting the CB/CD/DOP properties of glucose–chlorophyllin compound samples containing polystyrene microspheres. It is shown that the extracted CB value decreases linearly with the glucose concentration, while the extracted CD value increases linearly with the chlorophyllin concentration. However, the DOP is insensitive to both the glucose concentration and the chlorophyllin concentration. Consequently, the potential of the proposed SPR-enhanced Stokes–Mueller matrix polarimetry method for high-resolution CB/CD/DOP detection is confirmed. Notably, in contrast to conventional SPR techniques designed to detect relative refractive index changes, the SPR technique proposed in the present study allows absolute measurements of the optical properties (CB/CD/DOP) to be obtained.

A novel method for enhanced circular dichroism (CD) detection is proposed based on a surface plasmon resonance (SPR) prism coupler and a polarization scanning ellipsometry technique. An analytical model is derived to extract the CD and degree of polarization (DOP) properties of optical samples with and without scattering effects, respectively. The validity of the analytical model is confirmed by means of numerical simulations. The simulation results show that the proposed detection method has a sensitivity of 10 -5 ~10 -6 RIU (refractive index unit) for refractive indices in the range of 1.32~1.36 and 1.3100~1.3118. The practical feasibility of the proposed method is demonstrated by the experimental results for the sensitivity of the CD with the chlorophyllin samples with/without scattering effect. It is shown that for both types of sample, the extracted CD value increases linearly with the chlorophyll concentration over the considered range. In general, the results obtained in this study show that the measured CD response is highly sensitive to the polarization scanning angle. Consequently, the potential of polarization scanning ellipsometry for high-resolution CD detection is confirmed.

A dynamic polarization scanning ellipsometry technique based on Stokes polarimetry is proposed for dynamically characterizing a voltage-driven twisted nematic liquid crystal (TNLC) cell. In the proposed method, the six effective ellipsometric parameters are extracted under modulation voltages ranging from 0 V ~ + 10 V using four linearly polarized input lights. The profiles of the tilt angle and twist angle are calculated as a function of the modulation voltage. The validity of the proposed method is confirmed by comparing the experimental results for the effective ellipsometric parameters of a TNLC cell with the analytical results. Furthermore, a genetic algorithm (GA) based on a curve-fitting technique is used to inversely extract the pretilt angle, twist angle and rubbing direction of the TNLC cell. These extracted values are then compared to the known valued of the TNLC cell. In general, the results presented in this paper show that the proposed method provides a reliable means of obtaining the dynamic optical properties of a TNLC cell.