Portable Spectrophotometer Research Articles

Design of a Portable Spectrophotometer Based on Raspberry Pi for Tea Type Classification Using Machine Learning

Abstract The use of spectrophotometers in food and beverage quality analysis has become common. However, portability and high cost have long been a problem for food industry players and small-scale laboratories. In this study, a more compact and low-cost spectrophotometer has been developed using raspberry pi. To validate its performance, the prototype was used to classify green tea, black tea, and oolong tea types. The research started with designing and assembling the hardware using a raspberry pi camera with Complementary Metal Oxide Semiconductor (CMOS) sensor, Digital Versatile Disk (DVD) as diffraction grating, white LED as light source and 3D printer as casing. The prototype was then used to acquire data from green tea, black tea and oolong tea solutions. In the tea type identification process, Principal Component Analysis (PCA) was applied to the data obtained. The experiment involved 30 solution samples of each of the three types of tea. The light source was directed past the sample towards the slit and DVD, then the spectrum image of the light source was displayed through a user interface built using python programming. This research resulted in a spectrophotometer with dimensions of 260 mm x 120 mm x 63 mm that is capable of capturing light spectra in the range of 400 - 700 nm. Based on the experimental results, the classification accuracy of the three types of tea using CNN and Decision Tree reached 100%.

Evaluating the shelf life of strawberries using a portable Vis-NIR spectrophotometer and a Reflectance Quality Index (RQI)

This study aimed to develop a postharvest quality index for strawberry (Fragaria ananassa Duch.) based on Vis-NIR reflectance, using a portable handset spectrometer to assess fruit quality and establish maximum shelf life. Fresh strawberries were stored at different temperatures (5, 15, 25 and 35 °C) and at 60 % relative humidity for up to 15 days. Various quality parameters were evaluated, including colour, firmness, weight loss, appearance, and microbial quality, every three days during storage. Kinetic models were used to describe the changes in weight loss, appearance, colour, and firmness of strawberries during storage under different temperatures. Vis-NIR spectroscopy was employed to establish a Reflectance Quality Index (RQI) for determining fruit quality during storage. This index helped to reduce the high number of wavelengths to a lower set of representative independent variables. Calibration and prediction models were created using the partial least squares (PLS) regression method, incorporating cross-validation, to predict quality indicators of strawberries. These models were developed for predicting appearance, weight loss, L*, a*, b* colour parameters, and firmness. A very good fit was obtained (R2 = 0.95), demonstrating the feasibility of both the RQI and PLS for evaluating strawberry quality. This study presents a novel approach for assessing the shelf life of strawberries based on storage time and temperature using a portable spectrophotometer. This method can be a valuable tool for the food industry to ensure optimal fruit quality and minimize food losses.

Periodic Arrays of Plasmonic Ag-Coated Multiscale 3D-Structures with SERS Activity: Fabrication, Modelling and Characterisation.

Surface enhanced Raman spectroscopy (SERS) is gaining importance as sensing tool. However, wide application of the SERS technique suffers mainly from limitations in terms of uniformity of the plasmonics structures and sensitivity for low concentrations of target analytes. In this work, we present SERS specimens based on periodic arrays of 3D-structures coated with silver, fabricated by silicon top-down micro and nanofabrication (10 mm × 10 mm footprint). Each 3D-structure is essentially an octahedron on top of a pyramid. The width of the top part-the octahedron-was varied from 0.7 µm to 5 µm. The smallest structures reached an analytical enhancement factor (AEF) of 3.9 × 107 with a relative standard deviation (RSD) below 20%. According to finite-difference time-domain (FDTD) simulations, the origin of this signal amplification lies in the strong localization of electromagnetic fields at the edges and surfaces of the octahedrons. Finally, the sensitivity of these SERS specimens was evaluated under close-to-reality conditions using a portable Raman spectrophotometer and monitoring of the three vibrational bands of 4-nitrobenzenethiol (4-NBT). Thus, this contribution deals with fabrication, characterization and simulation of multiscale 3D-structures with SERS activity.

AN INTEGRATING SPHERE SPECTROPHOTOMETER PROTOTYPE

Spectroscopy is essential for research in the qualitative and quantitative analysis of samples. In recent years, the need to develop portable and accessible equipment for sample characterization has reduced real-time analysis. The objective is to design and build a prototype of a portable spectrophotometer with an integrating sphere using tools such as Rhinoceros and Trace-Pro for its theoretical design and radiation simulation. The result is an integrating sphere 3D printed with PLA material and an internal coating with a 95% high reflectance paint. The AS7265 Triad module is the detector, along with signal processing to obtain spectra from control samples. The spectral profiles obtained were comparable with commercial equipment, validating the viability of the measurement system as the first spectroscopic instrument in the field, in complement to other spectroscopic techniques. This breakthrough promises to facilitate research in the field and in environments where mobility and accessibility are priorities. Keywords: Integrating sphere, High reflectance coating, simulation Trace Pro, Manufacturing integrating sphere, spectroscopy.

Portable Spectrophotometer for Water Quality Monitoring in Recirculating Aquaculture Systems

Portable Spectrophotometer for Water Quality Monitoring in Recirculating Aquaculture Systems

Development of novel portable NIR spectroscopy process analytical technology (PAT) tool for monitoring the transition of ibuprofen to ibuprofen sodium during wet granulation process

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process.

The Effect of Non-Invasive Treatment Techniques on the Color Masking Ability and Surface Roughness of Induced Enamel Lesions (An in vitro Study).

The aim of this in vitro study was to evaluate the effect of the resin infiltration technique and remineralization of induced enamel caries with fluoride solution on the color masking of white spot lesions and surface roughness. A total of 45 sound teeth were used in this study. All the teeth were sectioned along the long axes into two halves, lingual and buccal to get 90 specimens. All specimens were immersed in a demineralized solution for 7 days. The specimens were divided randomly into three equal groups (n = 30) according to the type of treatment; Group 1 (n = 30) was treated with fluoride varnish (Clinpro); Group 2 (n = 30) was treated by resin infiltration (Icon); and Group 3 (n = 30) was used as a control group with no treatment. The color and surface roughness were measured three times at baseline (T1), directly after induction of artificial white spot lesions (WSLs) (T2), and directly after application of the treatment options (T3). The colors were measured using a portable reflective spectrophotometer and the 3D surface roughness was measured using a Light Sectioning Vision System. Data were collected and statistically analyzed using T test and Mann-Whitney U test. Surface roughness was almost equal in the study groups with no statistically significant differences reported. Icon showed slightly higher color scores than that of ClinPro. The Icon produced favorable esthetic results compared to the fluoride therapy while no significant differences were reported regarding the surface roughness.

Assessment of Drinking Water Quality from Source to Household Storage Containers in Kachisi town, West Shewa, Ethiopia

A wide range of human and natural processes affect the quality of fresh waters and diseases related to contamination of drinking water constitute a major burden on human health. Thus, this study was conducted to assess the quality of drinking water supplied to Kachisi town using selected physicochemical and bacteriological parameters. A total of 60 water samples in triplicate were collected from boreholes, water reservoirs, household taps and storage containers in the households between October, 2019 and February, 2020. The physiochemical parameters were measured using HACH HQ440d multi meter and portable digital spectrophotometer (DR/6000) according to HACH instruction and bacteriological parameters were analyzed using the membrane filtration technique. The study results indicated that temperature and pH of the water samples were in the range of 18.77 0C and 21.4 0C, and 7.05 and 7.73, respectively. Total dissolved solids fell between 93 mg/l and116 mg/l while electrical conductivity records ranged between 185µS/cm and 232 µS/cm. The concentrations of Iron, nitrate and phosphate were in the range between 0.01-0.26 mg/l, 2.20-5.72 mg/l and 0.12-0.76 mg/l, respectively. Total Coliforms loadalso varied from 1CFU/100ml to 2CFU/100ml in samples collected from boreholes and reservoirs, from 1CFU/100ml to 12CFU/100ml in tap water samples and from 6CFU/100ml to 22CFU/100ml in samples from storage containers in the households. Fecal Coliform was negative in the water samples collected from boreholes, reservoirs and household taps while it was detected in 25% of the samples collected from household storage containers in the rangeof 3CFU/100ml to 5CFU/100ml. The detection of Total Coliforms in all water samples and Faecal Coliforms in some of the household containers samples might be attributed to absence of disinfection, lack of regular supervision of the system, poor water management and lack of safe water storage and handling practices.

Lesion segmentation using 3D scan and deep learning for the evaluation of facial portwine stain birthmarks

BackgroundPortwine stain (PWS) birthmarks are congenital vascular malformations. The quantification of PWS area is an important step in lesion classification and treatment evaluation. AimsThe aim of this study was to evaluate the combination of 3D scan with deep learning for automated PWS area quantization. Materials and methodsPWS color was measured using a portable spectrophotometer. PWS patches (29.26–45.82 cm2) of different color and shape were generated for 2D and 3D PWS model. 3D images were acquired by a handheld 3D scanner to create texture maps. For semantic segmentation, an improved DeepLabV3+ network was developed for PWS lesion extraction from texture mapping of 3D images. In order to achieve accurate extraction of lesion regions, the convolutional block attention module (CBAM) and DENSE were introduced and the network was trained under Ranger optimizer. The performance of different backbone networks for PWS lesion extraction were also compared. ResultsIDeepLabV3+ (Xception) showed the best results in PWS lesion extraction and area quantification. Its mean Intersection over Union (MIou) was 0.9797, Mean Pixel Accuracy (MPA) 0.9908, Accuracy 0.9989, Recall 0.9886 and F1-score 0.9897, respectively. In PWS area quantization, the mean value of the area error rate of this scheme was 2.61 ± 2.33. ConclusionsThe new 3D method developed in this study was able to achieve accurate quantification of PWS lesion area and has potentials for clinical applications.

Feasibility assessment of a low-cost visible spectroscopy-based prototype for monitoring polyphenol extraction in fermenting musts.

Polyphenols have long been used to evaluate grape and wine quality and it is necessary to measure them throughout various winemaking stages. They are currently assessed predominantly through analytical methods, which are characterized by time-consuming procedures and environmentally harmful practices. Non-destructive spectroscopy-based devices offer an alternative but they tend to be costly and not readily accessible for smaller wineries. This study introduces the initial steps in employing a portable, user-friendly, and cost-effective visible (VIS) spectrophotometer prototype for direct polyphenol measurement during winemaking. Grapes (cv Syrah, Bobal, and Cabernet Sauvignon) at different maturation stages were fermented with or without stems. Throughout fermentation, parameters such as color intensity, total polyphenol index, total anthocyanins, and tannins were monitored. Concurrently, VIS spectra were acquired using both the prototype and a commercial instrument. Chemometric approaches were then applied to establish correlation models between spectra and destructive analyses. The prototype models demonstrated an acceptable level of confidence for only a few parameters, indicating their lack of complete reliability at this stage. Visible spectroscopy is already utilized for polyphenol analysis in winemaking but the aspiration to automate the process in wineries, particularly with low-cost devices, remains unrealized. This study investigates the feasibility of a low-cost and user-friendly spectrophotometer. The results indicate that, in the early stages of prototype utilization, the goal is attainable but requires further development and in-depth assessments. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Machine learning driven portable Vis-SWNIR spectrophotometer for non-destructive classification of raw tomatoes based on lycopene content

Most of the research on intact fruit spectroscopy is derivative in nature as it primarily showcase application of existing spectroscopy devices which are often proprietary in nature. The regression models developed by researchers to predict physicochemical attributes using spectra remain theoretical due to lack of mechanism to integrate the developed models back into proprietary devices. This poses challenge for commercial adaptation of this technology in commercial food quality supply chain. The present study addresses this research gap by presenting first of its kind innovative approach to classify tomatoes based on lycopene content using chemometrics-machine learning framework driven portable short-wave near infra-red (SWNIR) spectrophotometer developed by integration of open-source hardware (AS7265x multispectral chipset having wavelength range 410–940 nanometre (nm), Arduino Uno microcontroller) and software (R platform), housed in ergonomically designed and 3-dimension printed cabinet ensuring noise-free spectra acquisition. The lycopene content was observed to have strong negative correlation with wavelengths (nm) 485, 560 and 585 at ρ = – 0.65, – 0.70, – 0.70, whereas strong positive correlation with 760 nm at ρ = +0.64. Similar associations were qualitatively observed using principal component analysis. Atypical of literature, feature selection was performed based on analysis of variance and 14 wavelengths which exhibited statistically significant difference with respect to 15-days storage study (p ≤ 0.05) were selected for model development. Chemometrics-machine learning framework was used for development of optimised probabilistic and non-probabilistic models including logistic regression, Linear Discriminant Analysis (LDA), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models using 10-fold cross validation subjected to 80–20% train-test split of the dataset. In agreement with literature, 500–750 nm wavelength range dominated the classification of lycopene content. Notably, specific wavelengths for logistic regression (560 nm), LDA (730 nm, 645 nm, 560 nm, 535 nm), RF (760 nm, 585 nm, 560 nm, 645 nm), and ANN (585 nm, 560 nm) significantly influenced outcome instances across classifiers. Accuracy obtained from confusion matrix on test dataset was used as performance metric to compare different models. Logistic regression and RF showcased accuracy of 80%, LDA and SVM at 90% while ANN outperformed all models with accuracy of 95%. This study successfully augmented technological advancement in field of spectroscopy for non-invasive quality assessment of fruit. It is recommended to conduct similar studies on other climacteric fruits for wider adoption of this technology.

Fingerprinting based on spectral reflectance and chemometrics – An analytical approach aimed at combating the illegal trade of stingray meat in the Amazon

The survival of Amazon stingrays is threatened due to excessive fishing and habitat degradation. To address this issue, this study developed a groundbreaking method to authenticate and differentiate Amazon stingray meats using a portable spectrophotometer and chemometrics. Samples were collected from various species, including an endangered one with a commercialization ban and no population reduction records. Principal Component Analysis (PCA), identified natural groupings based on the meat's commercial origin, while Partial Least Squares-Discriminant Analysis (PLS-DA), accurately discriminated the commercial and geographic origins with 100 % accuracy. Moreover, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), effectively distinguished Amazon stingray meat from other marketable species. This approach offers a rapid, precise, and non-destructive means for monitoring and controlling the illegal trade of these species, thereby supporting decision-making in the field and promoting the conservation and sustainability of freshwater stingrays in the Amazon region.

Chemometrics driven portable Vis-SWNIR spectrophotometer for non-destructive quality evaluation of raw tomatoes

Most of the contemporary research published in field of visible-short wave near-infrared (Vis- SWNIR) fruit spectroscopy is ‘derivative’ in nature as they primarily showcase the application of existing spectroscopic devices coupled with traditional multivariate analysis techniques subjected to different fruits. The results of such studies often remain theoretical due to lack of integration mechanisms to incorporate developed models back into these proprietary devices. In present study, an open-source portable spectrophotometer has been proposed using commercially available multi-spectral sensor chipset integrated with microcontroller, housed in an ergonomically designed cabinet for spectral data acquisition in reflectance mode. During a 15-day post-harvest storage study of over 100 samples of raw tomatoes, the spectral data was acquired at 18 different wavelengths ranging from 410 nanometer (nm) to 940 nm, along with laboratory estimation 14 physicochemical attributes. Statistical and chemometrics analysis revealed that blue (380–440 nm) and green (440–600 nm) spectra varied with attributes associated with loss in water content, while red (600–750 nm) and SWNIR (750–1100 nm) spectra varied with attributes associated with carotenoid content, such as lycopene, antioxidant activity, and colour pigmentation. In order to predict physicochemical attributes using spectral wavelengths as regressor variables, various linear models including multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR) and non-linear models including random forest regression (RF), support vector machine regression (SVM), and artificial neural network (ANN) were developed using 10-fold cross-validation on 80-20% train-test split of the dataset. MLR was observed to have exhibited a linear relationship between lycopene and wavelengths 560 nm, 645 nm and 730 nm with highest coefficient of determination (R2) among linear models at 0.6361 (p < 0.05) but SVM outperformed all models with root mean squared error on test dataset, RMSE (Test) at 0.087 (p < 0.05). MLR was found to be best suitable for prediction of red colour pigmentation in tomatoes with 93.17% accuracy and lowest RMSE (Test) at 0.0608 (p < 0.05) for wavelengths 560 nm, 645 nm, 730 nm and 810 nm. While non-linear models demonstrated competence in capturing the underlying relationship between physicochemical attributes and spectra the optimised MLR models, albeit time and computationally dearer due to extensive pre-processing and criterion-based selection strategies, showed outstanding performance with high R2 and competitive RMSE (Test). Additionally, these MLR models offered interpretability to facilitate better understanding of spectral features that were influential in making predictions of carotenoid associated attributes, which is often absent in contemporary research papers. The present work augments technological advancement in portable fruit spectroscopy for rapid and non-invasive estimation of physicochemical attributes through chemometrics-machine learning framework.

Physicochemical Properties, Image Textures, and Relationships between Parameters of Red-Fleshed Apples Collected on Different Harvest Dates

The determination of the optimal harvest date is very important to obtain the desired apple quality. This study was aimed at evaluating the impact of the harvest date on selected properties of the following red-fleshed apples: ‘Alex Red’, ‘Trinity’, and ‘Roxana’. The color parameters of skin and flesh were measured using a portable spectrophotometer. The apple slices were imaged using a digital camera to extract flesh image textures. Then, the contents of chemical compounds, such as sugars, acids, and polyphenols were measured. The greatest significant changes with the delay in the harvest date were observed in the values of parameter a* of red-fleshed apple skin (from 21.57 to 25.42 for ‘Alex Red’, from 22.65 to 26.81 for ‘Trinity’, and from 21.82 to 30.00 for ‘Roxana’) and apple flesh (from 29.06 to 32.80 for ‘Alex Red’ and from 32.96 to 36.30 for ‘Trinity’); image textures (RHMean—from 227.6 to 236.5 for ‘Alex Red’, from 231.8 to 236.3 for ‘Trinity’, and from 217.8 to 227.9 for ‘Roxana’, and SHMean—from 126.0 to 135.6 for ‘Alex Red’, from 127.4 to 143.4 for ‘Trinity’, and from 58.4 to 96.0 for ‘Roxana’); the total sugar content—from 85.6 to 90.9 g kg−1 for ‘Alex Red’ and from 112 to 128 g kg−1 for ‘Trinity’; total pectin—from 6278 to 6008 mg·100 g−1 for ‘Alex Red’, from 6196 to 6401 mg·100 g−1 for ‘Trinity’, and from 5912 to 6807 mg·100 g−1 for ‘Roxana’; and total phenolic content—from 536.1 to 501.9 mg kg−1 for ‘Alex Red’, from 515.5 to 573.3 mg kg−1 for ‘Trinity’, and from 1121.3 to 1293.3 mg kg−1 for ‘Roxana’. The linear relationships with the Pearson’s correlation coefficients (R) reaching 0.999 between the image texture from color channel a and color parameter L*, and −0.999 for the image feature from color channel R vs. color parameter a* of the flesh, and image texture from channel X vs. anthocyanins were confirmed.

Development of a portable spectrophotometer using artificial neural networks for non-invasive determination of glycated hemoglobin in blood by Raman spectroscopy

BACKGROUND: Non-invasive diagnosis of diabetes is one of the major problems of contemporary medicine. The system being planned could be a new technology for measuring hemoglobin A1c (HbA1c) accurately and non-invasively. Therefore, a series of studies are to be conducted to assess the efficiency of the method under study and determine its potential for medical diagnosis and monitoring of HbA1c.&#x0D; AIMS:&#x0D; &#x0D; Investigation of the feasibility of Raman spectroscopy for non-invasive measurement of HbA1c.&#x0D; Development and design of a portable analyzer using this technology.&#x0D; Assessment of the efficiency and accuracy of the developed device.&#x0D; &#x0D; METHODS: Neural network creation requires collecting a training sample of measurements for subsequent application of TensorFlow library tools and performing laboratory measurements to calibrate the system for determining HbA1c. The device will use a 785-nm laser to take spectra according to the Raman spectroscopy. The obtained data will be fed to the input of the neural network based on the architecture of convolutional neural networks. Experiments will be conducted to train the model to determine the accuracy and efficiency of the device. A two-step data collection procedure is planned. First, a preliminary test will be done on 50 patients to see how the proposed method handles different age and gender groups and different HbA1c levels. Later, the data will continue to be collected on a larger scale, including patients with different types of diabetes and healthy individuals. Data will be collected using a portable spectrophotometer and monitored by high-performance liquid chromatography. Various metrics will be used to assess the efficiency and accuracy of the device such as accuracy, precision, recall, and F1-score.&#x0D; RESULTS: An analysis of the available literature was conducted and the following conclusions were drawn. In addition, a neural network model was developed using HbA1c measurements. Currently, our model is optimized to improve the accuracy and reliability of the results.&#x0D; CONCLUSIONS: The non-invasive Raman spectroscopy-based method has several advantages in measuring HbA1c levels. The procedure is faster and non-traumatic, and HbA1c levels can be monitored continuously. In particular, the non-invasive method eliminates errors associated with protein leakage outside the bloodstream.

Physicochemical properties of peach fruit assessed by non-destructive methodology related to the development of Monilinia fructicola during postharvest

ABSTRACT The concomitant evaluation of physicochemical properties and development of important diseases, such as brown rot in peaches postharvest, is hampered by the use of destructive methods for physicochemical analysis. Therefore, this study aimed to evaluate the physicochemical characteristics in wounded and unwounded ‘Chimarrita’ and ‘Maciel’ peaches inoculated with M. fructicola assessed by non-destructive methods. The fruits were stored at room temperature (25 ºC) for 10 days after harvest. Colorimeter, near-infrared spectroscopy and portable spectrophotometer DA-meter® (single index of absorbance difference, IAD index) were used to evaluate the physicochemical changes that naturally occurred in peaches during postharvest and due to pathogen development on the same fruit simultaneously. Incidence and severity of brown rot were evaluated by counting the number of lesions resulting from inoculation at 24, 48 and 60 h post inoculation. Also, the sporulation of M. fructicola on fruit surface was evaluated. The results indicated that differences between wounded and unwounded fruits related soluble solids content, pulp firmness, titratable acidity, dry matter and IAD did not affect the incidence of M. fructicola in both cultivars. However, the interaction between dry matter and colour at the time of inoculation influenced colonisation and consequent lesion size in inoculated wounded ‘Chimarrita’ fruits.

The Effect of Cysteine Peptide Ingestion on Skin Brightness, a Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Human Clinical Trial

Glutathione (GSH) is present in almost all human cells and has a beneficial effect on human skin brightness. Cysteinylglycine (Cys-Gly) and γ-glutamylcysteine (γ-Glu-Cys) are GSH synthesis components. In this study, we defined glutathione (GSH), cysteinylglycine (Cys-Gly), and γ-glutamylcysteine (γ-Glu-Cys) as cysteine peptide and performed a randomized, double-blind, placebo-controlled study to investigate the effects of orally administered cysteine peptide on human skin brightness using a CM-26d portable spectrophotometer in healthy males and females aged between 20 and 65 years old. Eligible participants were randomly allocated into three groups (cysteine peptide 45 mg: n = 16, 90 mg: n = 15, and placebo: n = 16). Each subject ingested six tablets every day for 12 weeks, and skin brightness was measured at 0, 4, 8, and 12 weeks. As a result, the 45 mg group exhibited arm brightening in a time-dependent manner, and a significant difference was observed compared to the placebo at week 12 (p = 0.028). Moreover, no serious adverse events and changes related to 270 mg study food were observed in the safety trial. Here, we suggest that cysteine peptide is a promising and safe compound for human skin brightness.

Smart scientific instruments based on smartphones: a brief review

&lt;span&gt;Smartphone has gone beyond a communication hub to be a measurement device itself, thanks to various built-in sensors. This article reviewed achievements in transforming ubiquitous smartphones into cost-effective scientific instruments for educational laboratories, environmental studies, point-of-care diagnostics, home-based health monitoring, and rehabilitation. Magnetic fields were precisely measured by built-in magnetometers, leading to demonstrations for engineering and medical applications. The smartphone-based joint-angle measurement was a viable alternative to traditional goniometers. Characterizations of optical signals captured by cameras led to portable spectrophotometers and colorimeters for both educational and practical uses. Interestingly, smartphones became a platform for high-resolution microscopes and fluorescence microscopes were developed with add-on components. These smart instruments become even more attractive options in the pandemic period with limited facility and laboratory access.&lt;/span&gt;

Point-of-Care Portable 3D-Printed Multispectral Sensor for Real-Time Enzyme Activity Monitoring in Healthcare Applications

Absorbance spectroscopy finds many biomedical and physical applications ranging from studying the atomic and molecular details of the analyte to determination of unknown biological species and their concentration or activity in the samples. Commercially available laboratory-based spectrometers are usually bulky and require high power and laborious manual processing, making them unsuitable to be deployed in portable and space-constrained environments, thereby further limiting their utility for real-time on-site monitoring. To address these challenges, here we developed a portable 3D-printed multispectral spectrophotometer based on absorbance spectroscopy for real-time monitoring of enzyme molecular activity. Monitoring enzyme (such as tyrosinase) activity is critical, as it quantifies its reaction rate, which is dependent on many factors such as the enzyme and substrate concentrations, temperature, pH, and other regulators such as inhibitors and effectors. Tyrosinase is a critical enzyme responsible for melanin synthesis in living beings and exhibits enzymatic browning in fruits and vegetables. It finds various commercial applications in the fields of healthcare (skin pigmentation, wound healing, etc.), forensics, and food processing. Here, tyrosinase activity was monitored using a 3D-printed spectral sensor at different rates and compared against measurements obtained from laboratory instruments. The enzyme activity was also studied using kojic acid (i.e., a commonly employed commercial tyrosinase inhibitor) while varying its molar and volume concentrations to control the reaction rate at discrete activity levels. For tyrosinase activity monitoring, the fabricated device has shown significant correlation (R2 = 0.9999) compared to measurements from the standard table-top spectrophotometer. We also provide a performance comparison between the 3D-printed and the laboratory spectrophotometer instruments by studying tyrosinase enzyme activity with and without the influence of an inhibitor. Such a device can be translated into various absorbance spectroscopy-based point-of-care biomedical and healthcare applications.

Molecular interaction of black tea components with phospholipid vesicles

The molecular interaction of black tea components with phospholipid vesicles has been investigated to elucidate the various functions of black tea infusions, such as astringency, inhibition of cholesterol absorption, and inactivation of viruses. We have developed a simple method to analyze the turbidity of phospholipid vesicle solutions reacted with black tea infusions or components as an indicator of the molecular interaction. Using a portable visible spectrophotometer, the dose-dependency of catechins, theaflavins, thearubigins, and black tea infusions on turbidity was analyzed. The results indicate that authentic catechins and theaflavins independently caused aggregation of the phospholipid vesicles; however, their concentrations in the tea infusions were insufficient. Meanwhile, thearubigins prepared from black tea leaves caused aggregation at the concentration found in the tea infusions. It is concluded that thearubigins, instead of theaflavins, mainly contribute to the interaction with phospholipid vesicles.