Two-trace two-dimensional(2T2D) correlation spectroscopy application in food safety: A review

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic methods. Near-infrared spectroscopy (NIRS) offers unique advantages in probing molecular vibrational information from blood, effectively capturing potential structural changes in haemoglobin (Hb) in lung cancer patients. In this study, we address the challenge of detecting subtle Hb features within the broader blood matrix and introduce an innovative two-stage spectral analysis framework. First, continuous wavelet transform (CWT) is employed to enhance spectral resolution and reinforce the key absorption bands of Hb. Subsequently, two-trace two-dimensional correlation spectroscopy (2T2D-COS) is applied to examine the fine vibrational differences-in both synchronous and asynchronous spectra-between lung cancer patients and healthy controls, revealing alterations in Hb secondary structures (e.g., α-helices and β-sheets). Results show that critical Hb-related peaks at 4862 cm-1, 4615 cm-1, and 4432 cm-1 undergo significant changes in lung cancer samples. Furthermore, combining these refined spectral features with machine learning classifiers (e.g., support vector machines) achieves an overall accuracy of 97.50 % and a sensitivity of 100.00 %. This work not only confirms the value of NIRS in detecting protein-level molecular information in blood but also presents a promising, efficient spectroscopic strategy for early lung cancer diagnosis, offering broad biomedical applicability.

Combining two-trace two-dimensional correlation analysis and convolutional autoencoder-based feature extraction from an entire correlation map to enhance vibrational spectroscopic discrimination of geographical origins of agricultural products

This study explored convolutional autoencoder (CAE)-based feature extraction from entire two-trace two-dimensional (2T2D) correlation maps as a promising tool to enhance the accuracy of vibrational spectroscopy-based discriminant analysis. Although 2T2D correlation maps constructed using only a pair of spectra were effective to highlight minute spectral differences, there was an excessive number of features (variables). Thus, only slice spectra at a wavenumber chosen from the map were typically used for discriminant analysis. In this case, exclusion of a huge number of remaining 2T2D features that would be complementary and descriptive for a given analysis was a major drawback limiting accuracy. Therefore, CAE was adopted to extract features from entire 2T2D correlation maps while minimizing information loss. For evaluation, near-infrared (NIR) and Raman spectra of chili pepper samples and NIR spectra of perilla seed samples were employed for hetero- and homo-spectral 2T2D correlation analysis, respectively. Then, CAE-extracted features from the 2T2D correlation maps were used to discriminate the geographical origins of samples using support vector machine (SVM). Accuracy improved by employing CAE-extracted variables in both cases compared with those using slice spectra chosen from the 2T2D maps. Moreover, to provide clearer insight into the models, gradient-weighted class activation mapping (Grad-CAM) identifying the variables significantly contributed to the discrimination was employed in parallel.

Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion

INTRODUCTION

Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI.

OBJECTIVE

Realization of rapid and accurate identification of the origin of G. elata BI.

MATERIALS AND METHODS

Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI.

SAMPLES

Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images.

RESULTS

The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness.

CONCLUSION

Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.

Phase of the second-order susceptibility in vibrational sum frequency generation spectroscopy: Origins, utility, and measurement techniques

Vibrational sum frequency generation can provide valuable structural information at surfaces and buried interfaces. Relating the measured spectra to the complex-valued second-order susceptibility χ(2) is at the heart of the technique and a requisite step in nearly all subsequent analyses. The magnitude and phase of χ(2) as a function of frequency reveal important information about molecules and materials in regions where centrosymmetry is broken. In this tutorial-style perspective, the origins of the χ(2) phase are first described, followed by the utility of phase determination. Finally, some practical methods of phase extraction are discussed.

Optimization of the selection of suitable harvesting periods for medicinal plants: taking Dendrobium officinale as an example

BACKGROUND

Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today's medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period.

RESULTS

The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R2 = 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R2 = 0.99, RPD = 9.55.

CONCLUSIONS

These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.

A new effective method for identifying boletes species based on FT-MIR and three dimensional correlation spectroscopy projected image processing

This study proposed the necessity of identifying the species for boletes in combination with the medicinal value, nutritional value and the problems existing in the industrial development of boletes. Based on the preprocessing of Fourier transform mid-infrared spectroscopy (FT-MIR) by 1st, 2nd, SNV, 2nd + MSC and 2nd + SG, Multilayer Perceptron (MLP) and CatBoost models were established. To avoid complex preprocessing and feature extraction, we try deep learning modeling methods based on image processing. In this paper, the concept of three-dimensional correlation spectroscopy (3DCOS) projection image was proposed, and 9 datasets of synchronous, asynchronous and integrative images are generated by computer method. In addition, 18 deep learning models were established for 9 image datasets with different sizes. The results showed that the accuracy of the three types of synchronous spectral models reached 100%, while the accuracy of the asynchronous spectral and integrative spectral models of 3DCOS projection images were 96.97% and 97.98% in the case of big datasets, which overcame the defects of poor modeling effect of asynchronous spectral and integrative spectral in previous two-dimensional correlation spectroscopy (2DCOS) studies. In conclusion, the modeling results of 3DCOS projection images are perfect, and we can apply this method to other identification fields in the future.

Machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of Boletus tomentipes

This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (B.tomentipes) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting Machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.

Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning

INTRODUCTION

The cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs. Panax notoginseng is a widely used Chinese medicinal material. The growth and accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing.

METHODS

In this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of P. notoginseng. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of P. notoginseng at two different levels (district and town levels).

RESULTS

The results indicated that the contents of saponins in P. notoginseng are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for P. notoginseng geographic origin traceability, even if the distance between sampling points is small.

DISCUSSION

The findings of this study could improve the quality of P. notoginseng, provide a reference for cultivating P. notoginseng in the future and alleviate the occurrence of market fraud.

Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species

Boletes are favored by consumers because of their unique flavor, rich nutrition and delicious taste. However, the different nutritional values of each species lead to obvious price differences, so shoddy products appear on the market, which affects food safety. The aim of this study was to find a rapid and effective method for boletes species identification. In this paper, 1,707 samples of eight boletes species were selected as the research objects. The original Mid-Infrared (MIR) spectroscopy data were adopted for support vector machine (SVM) modeling. The 11,949 spectral images belong to seven data sets such as two-dimensional correlation spectroscopy (2DCOS) and three-dimensional correlation spectroscopy (3DCOS) were used to carry out Alexnet and Residual network (Resnet) modeling, thus we established 15 models for the identification of boletes species. The results show that the SVM method needs to process complex feature data, the time cost is more than 11 times of other models, and the accuracy is not high enough, so it is not recommended to be used in data processing with large sample size. From the perspective of datasets, synchronous 2DCOS and synchronous 3DCOS have the best modeling results, while one-dimensional (1D) MIR Spectrum dataset has the worst modeling results. After comprehensive analysis, the modeling effect of Resnet on the synchronous 2DCOS dataset is the best. Moreover, we use large-screen visualization technology to visually display the sample information of this research and obtain their distribution rules in terms of species and geographical location. This research shows that deep learning combined with 2DCOS and 3DCOS spectral images can effectively and accurately identify boletes species, which provides a reference for the identification of other fields, such as food and Chinese herbal medicine.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments

This comprehensive survey review compiles noteworthy developments and new concepts of two-dimensional correlation spectroscopy (2D-COS) for the last two years. It covers review articles, books, proceedings, and numerous research papers published on 2D-COS, as well as patent and publication trends. 2D-COS continues to evolve and grow with new significant developments and versatile applications in diverse scientific fields. The healthy, vigorous, and diverse progress of 2D-COS studies in many fields strongly confirms that it is well accepted as a powerful analytical technique to provide an in-depth understanding of systems of interest.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS): Part III. Versatile applications

In this review, the comprehensive summary of two-dimensional correlation spectroscopy (2D-COS) for the last two years is covered. The remarkable applications of 2D-COS in diverse fields using many types of probes and perturbations for the last two years are highlighted. IR spectroscopy is still the most popular probe in 2D-COS during the last two years. Applications in fluorescence and Raman spectroscopy are also very popularly used. In the external perturbations applied in 2D-COS, variations in concentration, pH, and relative compositions are dramatically increased during the last two years. Temperature is still the most used effect, but it is slightly decreased compared to two years ago. 2D-COS has been applied to diverse systems, such as environments, natural products, polymers, food, proteins and peptides, solutions, mixtures, nano materials, pharmaceuticals, and others. Especially, biological and environmental applications have significantly emerged. This survey review paper shows that 2D-COS is an actively evolving and expanding field.

Exploring the use of infrared absorption spectroscopy and two-trace two-dimensional correlation analysis for the resolution of multi-component drug mixtures

Community drug checking provides an essential service that responds to the unpredictable and variable supply of illicit drugs. Point of care detection of trace components using portable infrared spectrometers is a harm reduction measure to prevent overdose. This study investigates the ability of weighted subtraction and two-trace two-dimensional (2T2D) correlation analysis to reveal the presence of heroin in an opioid mixture that contains heroin and fentanyl mixed with caffeine as a cutting agent. In both methods, a spectral trace was identified that provided reasonably high correlation scores to heroin when compared to entries in drug libraries. The two-trace correlation analysis produced a higher match score, suggesting that future improvements in spectral unmixing methods may enhance the reliability of detecting trace components in drugs.

Slice spectra approach to synchronous Two-dimensional correlation spectroscopy analysis for milk adulteration discriminate

The discrimination approach of adulterated milk was proposed combined synchronous two-trace two-dimensional (2T2D) correlation slice spectra at the characteristic wavebands of adulterant in milk with multivariate method. Two common adulterants, melamine and urea, were analyzed to demonstrate useful by the method. 2T2D (near infrared) NIR slice spectra at characteristic wavebands of adulterant were extracted from the synchronous 2T2D correlation spectra, and were input to construct the N-way partial least squares discriminant analysis (NPLS-DA) models. One-dimensional (1D) spectroscopy featuring all the present components in the samples combined with partial least squares discriminant analysis (PLS-DA) was also evaluated for comparison. The results indicated that for one kind of adulterant in model, prediction accuracies of slice spectral models were both 100% for melamine-adulterated and urea-adulterated samples discrimination. Moreover, for two kinds of adulterants in model, prediction accuracies of slice spectral models were 90.57% and 100% for melamine-adulterated and urea-adulterated discrimination, respectively, which was better than those of 1D whole models based on PLS-DA (only 81.13% and 98.15%, respectively). The comparison informs that the 2T2D slice spectra extracted at the characteristic wavebands of adulterant highlighted the adulterant spectral features and was obviously advantage to improve the discrimination accuracy. Meanwhile, the complexity of slice spectra is significantly reduced compared with the whole matrix of synchronous 2T2D correlation spectra.

Discrimination of adulterated milk using temperature-perturbed two-dimensional infrared correlation spectroscopy and multivariate analysis

The discrimination method for adulterated milk is proposed based on temperature-perturbed two-dimensional (2D) infrared correlation spectroscopy and N-way partial least squares discriminant analysis (NPLS-DA). Two brands of pure and adulterated milk samples were prepared. The mid-infrared spectra of all samples were obtained from 30 ℃ to 55 ℃ with an interval of 5 ℃. Under the perturbation of temperature, synchronous 2D correlation spectra were calculated to build discrimination models of pure milk and adulterated milk. In comparison, the NPLS-DA models were built based on three-dimensional (3D) stacked map (sample × temperature × wavenumber variable). For the NPLS-DA models of two brands of milk, the discrimination accuracy of unknown samples in the prediction set is 100% using temperature-perturbed 2D infrared correlation spectra, versus 77.8% using conventional 3D stacked map. The proposed method can be used as an alternative way for classifying pure and adulterated milk.

A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves

BACKGROUND

Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy.

RESULTS

(1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions.

CONCLUSIONS

This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus.

Rapid and non-destructive freshness evaluation of squid by FTIR coupled with chemometric techniques

BACKGROUND

Freshness is an important quality of squid with respect to determining the market price. The methods of evaluation of freshness fail to be widely used as a result of the lack of rapidity and quantitation. In the present study, a rapid and non-destructive quantification of squid freshness by Fourier transform infrared spectroscopy (FTIR) spectra combined with chemometric techniques was performed.

RESULTS

The relatively linear content change of trimethylamine (TMA-N) and dimethylamine (DMA-N) of squid during storage at 4 °C indicated their feasibility as a freshness indicator, as also confirmed by sensory evaluation. The spectral changes were mainly caused by the degradation of proteins and the production of amines by two-dimensional infrared correlation spectroscopy, among which TMA-N, DMA-N and putrescine were the main amines. The successive projections algorithm (SPA) was employed to select the sensitive wavenumbers to freshness for modeling prediction including partial least-squares regression, support vector regression (SVR) and back-propagation artificial neural network. Generally, the SPA-SVR model of the selected characteristic wavenumber showed a higher prediction accuracy for DMA-N (R² _P  = 0.951; RMSE_P  = 0.218), whereas both SPA-SVR (R² _P  = 0.929; RMSE_P  = 2.602) and Full-SVR (R² _P  = 0.941; RMSE_P  = 2.492) models had a higher predictive ability of TMA-N.

CONCLUSION

The results of the present study demonstrate that FTIR spectroscopy coupled with multivariate calibration shows significant potential for the prediction of freshness in squid. © 2021 Society of Chemical Industry.

Influence of brands on a discrimination model for adulterated milk based on asynchronous two-dimensional correlation spectroscopy slice spectra

To improve the robustness of near infrared (NIR) identification models for the milk adulteration, a novel approach was explored based on asynchronous two-dimensional correlation spectroscopy (2D-COS) slice spectra obtained at characteristic wavebands for pure milk and adulterant combined with an N-way partial least squares discriminant analysis (NPLS-DA). NIR diffuse reflectance spectra from four different brands, Guangming (GM), Mengniu (MN), Sanyuan (SY), and Wandashan (WDS), were collected in range of 11,000 to 4000 cm^-1. Influence of brands on discrimination models for adulterated milk was analyzed. The asynchronous 2D-COS slice spectra at 10 characteristics wavebands, including 4 wavebands for pure milk and 6 wavebands for urea, were input into NPLS-DA to construct discriminant models. External validations using five independent calibration sets from intrabrand or interbrand were established. The same prediction set of 26 SY samples was used to assess the prediction ability of different calibration sets and compared with traditional one-dimensional (1D) NIR spectra based on a partial least squares discriminant analysis (PLS-DA). It showed that for intrabrand model, the correct rates for the calibration and predication sets were 100% and 96.15%, respectively. For the interbrand model, the correct rates by the NPLS-DA for the calibration set of GM, MN, and WDS milk were both 100%. The corresponding rates for the prediction set were 73%, 88.46% and 69.23%, respectively, which were much higher than those of PLS-DA (only 50%, 53.83% and 50%, respectively). It was proven that model robustness was sensitive to changes in the milk brands. The proposed method can effectively reduce the influence of brands on the discrimination models.

Discrimination of Adulterated Ginkgo Biloba Products Based on 2T2D Correlation Spectroscopy in UV-Vis Range

Ginkgo biloba is a popular medicinal plant widely used in numerous herbal products, including food supplements. Due to its popularity and growing economic value, G. biloba leaf extract has become the target of economically motivated adulterations. There are many reports about the poor quality of ginkgo products and their adulteration, mainly by adding flavonols, flavonol glycosides, or extracts from other plants. In this work, we developed an approach using two-trace two-dimensional correlation spectroscopy (2T2D COS) in UV-Vis range combined with multilinear principal component analysis (MPCA) to detect potential adulteration of twenty G. biloba food supplements. UV-Vis spectral data are obtained for 80% methanol and aqueous extracts in the range of 245–410 nm. Three series of two-dimensional correlation spectra were interpreted by visual inspection and using MPCA. The proposed relatively quick and straightforward approach successfully differentiated supplements adulterated with rutin or those lacking ginkgo leaf extract. Supporting information about adulteration was obtained from the difference between the DPPH radical scavenging capacity of both extracts and from chromatographic (HPLC-DAD) fingerprints of methanolic samples.

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm^–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm^–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm^–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

Ultralow sidelobe midinfrared optical phased array based on a broadband metasurface

In this paper, we propose an all-solid-state and ultralow sidelobe optical phased array (OPA) through designing a broadband angle-insensitive reflective metasurface in the midinfrared. The simulation results show that the metasurface can realize the wide-frequency metareflection characteristics in the range of 4.3∼5.0µm. Notably, the metasurface array can almost generate a continuous sweep between 0° and 342°, while the variation of reflectivity amplitude is only 10.2%, by changing the corresponding structural parameters. Then, we design and simulate an OPA based on these excellent characteristics of the broadband metasurface. By simply changing the periodicity of the OPA structure, the continuous deflection angles can be achieved within 29.41°, which can increase to 44.06° by changing the angle of the incident beam. A key feature of our design is that the sidelobe energy is less than 3.10% of the main lobe energy.

Feasibility of discrimination of gall bladder (GB) stone and GB polyp using voltage-applied SERS measurement of bile juice samples in conjunction with two-trace two-dimensional (2T2D) correlation analysis

Voltage-applied SERS measurement of bile juice in conjunction with two-trace two-dimensional (2T2D) correlation analysis was demonstrated as a potential tool to enhance discrimination of gall bladder (GB) stone and GB polyp. When SERS spectra of the aqueous phases extracted from raw bile juice samples were measured with applying external voltage from -300 to +300 mV (100 mV intervals), subsequent spectral variations of the adsorbed components (bilirubin-containing compounds) on the SERS substrate were minute, and discrimination of the two GB diseases in a principal component score domain was difficult. Therefore, 2T2D correlation analysis, effectively identifying asynchronous (dissimilar) spectral behaviors in the voltage-induced SERS spectra, was used to improve the discrimination. When two spectra of a sample collected with application of +100 and +300 mV were adopted, the features of subsequent 2T2D slice spectra were characteristic, and discrimination of GB stone and GB polyp substantially improved. External voltage application and recognition of the voltage-induced spectral features by 2T2D correlation analysis were key factors for the improvement. Since the demonstrated method relied on only a few SERS-active compounds, infrared (IR) spectroscopy featuring all the present components in the samples was also evaluated for comparison. However, the IR-based discrimination was inferior because the metabolite compositions in the samples between the GB diseases were not noticeably different.