Detection of l -Cysteine in wheat flour by Raman microspectroscopy combined chemometrics of HCA and PCA

Analytical developments in the synergism of copper particles and cysteine: a review

Cysteine, a sulfur-containing amino acid, is a vital candidate for physiology. Coinage metal particles (both clusters and nanoparticles) are highly interesting for their spectacular plasmonic properties. In this case, copper is the most important candidate for its cost-effectiveness and abundance. However, rapid oxidation destroys the stability of copper particles, warranting the necessity of suitable capping agents and experimental conditions. Cysteine can efficiently carry out such a role. On the contrary, cysteine sensing is a vital step for biomedical science. This review article is based on a comparative account of copper particles with cysteine passivation and copper particles for cysteine sensing. For the deep understanding of readers, we discuss nanoparticles and nanoclusters, properties of cysteine, and importance of capping agents, along with various synthetic protocols and applications (sensing and bioimaging) of cysteine-capped copper particles (cysteine-capped copper nanoparticles and cysteine-capped copper nanoclusters). We also include copper nanoparticles and copper nanoclusters for cysteine sensing. As copper is a plasmonic material, fluorometric and colorimetric methods are mostly used for sensing. Real sample analysis for both copper particles with cysteine and copper particles for cysteine sensing are also incorporated in this review to demonstrate their practical applications. Both cysteine-capped copper particles and copper particles for cysteine sensing are the main essence of this review. The aspect of the synergism of copper and cysteine (unlike other amino acids) is quite promising for future researchers.

SLE diagnosis research based on SERS combined with a multi-modal fusion method

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Food adulteration: Causes, risks, and detection techniques-review

Food adulteration is the intentional addition of foreign or inferior substances to original food products for a variety of reasons. It takes place in a variety of forms, like mixing, substitution, hiding poor quality in packaging material, putting decomposed food for sale, misbranding or giving false labels, and adding toxicants. Several analytical methods (such as chromatography, spectroscopy, electronic sensors) are used to detect the quality of foodstuffs. This review provides concise but detailed information to understand the scope and scale of food adulteration as a way to further detect, combat, and prevent future adulterations. The objective of this review was to provide a comprehensive overview of the causes, risks, and detection techniques associated with food adulteration. It also aimed to highlight the potential health risks posed by consuming adulterated food products and the importance of detecting and preventing such practices. During the review, books, regulatory guidelines, articles, and reports on food adulteration were analyzed critically. Furthermore, the review assessed key findings to present a well-rounded analysis of the challenges and opportunities associated with combating food adulteration. This review included different causes and health impacts of food adulteration. The analytical techniques for food adulteration detection have also been documented in brief. In addition, the review emphasized the urgency of addressing food adulteration through a combination of regulatory measures, technological advancements, and consumer awareness. In conclusion, food adulteration causes many diseases such as cancer, liver disease, cardiovascular disease, kidney disease, and nervous system-related diseases. So, ensuring food safety is the backbone of health and customer satisfaction. Strengthening regulations, taking legal enforcement action, enhancing testing, and quality control can prevent and mitigate the adulteration of food products. Moreover, proper law enforcement and regular inspection of food quality can bring about drastic changes.

Metal-assisted core-shell plasmonic nanoparticles for small molecule biothiol analysis and enantioselective recognition

Cysteine (Cys) enantiomorphs, important small-molecule biothiols, participate in various antioxidative, flavoring, and poison-removing processes in the food industry. Current cysteine enantiomorph analysis methods require effective strategies for distinguishing them due to their similar structures and reactivity. Herein, we present a metal ion-assisted enantiomorph-selective surface-enhanced Raman scattering (SERS) biosensor based on an amphiphilic polymer matrix (APM), which can promote cysteine enantiomorph (L/D-Cys) identification. The highly selective molecular orientation is perhaps caused by the intermolecular hydrogen bonding with chiral isomers (metal centers). The experimental results show that the SERS biosensor has a sensitivity-distincting factor toward L-Cys and D-Cys. The linear range is from 1 mmol L-1 to 1 nmol L-1, along with a low limit of detection of 0.77 pmol L-1. Moreover, the fabricated Cu-APM biosensor exhibits remarkable stability and high repeatability, with an RSD of 3.7%. Real food cysteine enantiomorph detection was performed with L-Cys-containing samples of onion, cauliflower, garlic, and apple, and D-Cys-containing samples of vinegar, black garlic, cheese, and beer. The results show that the Cu-APM biosensor can be utilized as a powerful tool for real-time determination of Cys enantiomorphs in different food samples. Thus, the metal-ion-assisted enantiomorph-selective SERS biosensor has potential as an adaptable tool for enantiomorph detection and food sample analysis.

A self-cascade system based on Ag nanoparticle/single-walled carbon nanotube nanocomposites as an enzyme mimic for ultrasensitive detection of L-cysteine

L-Cysteine, widely used in medicine and the food industry, is of great essentiality to organisms and the food quality. Given that current detection approaches require exacting lab conditions and tedious sample treatment, there is a pressing demand for developing a method that possesses advantages of user friendliness, prominent performance, and cost-effectiveness. Herein, a self-cascade system was developed for the fluorescence detection of L-cysteine based on the ingenious performance of Ag nanoparticle/single-walled carbon nanotube nanocomposites (AgNP/SWCNTs) and DNA-templated Ag nanoclusters (DNA-AgNCs). The fluorescence of DNA-AgNCs could be quenched on account of the adsorption of DNA-AgNCs on AgNP/SWCNTs by π-π stacking. With the cooperation of Fe²⁺, AgNP/SWCNTs with oxidase and peroxidase-like activities could catalyze the oxidation of L-cysteine to produce cystine and hydrogen peroxide (H₂O₂) and then break the O-O bond of H₂O₂ to generate a hydroxyl radical (·OH), which could cleave the DNA strand into different sequence fragments which subsequently peeled off from the AgNP/SWCNTs, resulting in a "turn-on" fluorescence response. In this paper, AgNP/SWCNTs with multi-enzyme activities was synthesized enabling the reaction to proceed in just one step. The successful preliminary applications for the L-cysteine detection in pharmaceutical, juice beverage, and serum samples indicated that the developed method exhibited great potential in medical diagnosis, food monitoring, and the biochemical field, which also broadened the horizon for follow-up research.

Monitoring the lipid oxidation and fatty acid profile of oil using algorithm-assisted surface-enhanced Raman spectroscopy

Deep-fat frying of food develops lipid oxidation products that deteriorate oil and pose a health risk. This necessitates the development of a rapid and accurate oil quality and safety detection technique. Herein, surface-enhanced Raman spectroscopy (SERS) and sophisticated chemometric techniques were used for rapid and label-free determination of peroxide value (PV) and fatty acid composition of oil in-situ. In the study, plasmon-tuned and biocompatible Ag@Au core-shell nanoparticle-based SERS substrates were used to obtain optimum enhancement despite matrix interference to efficiently detect the oil components. The potent combination of SERS and the Artificial Neural Network (ANN) method could determine the fatty acid profile and PV with upto 99% accuracy. Moreover, the SERS-ANN method could quantify the low level of trans fats, i.e., < 2%, with 97% accuracy. Therefore, the developed algorithm-assisted SERS system enabled the sleek and rapid monitoring and on-site detection of oil oxidation.

Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning

The salinity level of the growing medium has diverse effects on the development of plants, including both physical and biochemical changes. To determine the salt stress level of a plant endures, one can measure these structural and chemical changes. Raman spectroscopy and biochemical analysis are some of the most common techniques in the literature. Here, we present a combination of machine learning and Raman spectroscopy with which we can both find out the biochemical change that occurs while the medium salt concentration changes and predict the level of salt stress a wheat sample experiences accurately using our trained regression models. In addition, by applying different machine learning algorithms, we compare the level of success for different algorithms and determine the best method to use in this application. Production units can take actions based on the quantitative information they get from the trained machine learning models related to salt stress, which can potentially increase efficiency and avoid the loss of crops.

The effects of different protease treatments on the techno-functional, structural, and bioactive properties of bovine casein

In this study, bovine sodium caseinate (NaCas) was hydrolyzed with four proteases, alcalase, savinase, subtilisin A, and flavourzyme. In addition to the structural changes occurred through the enzymatic hydrolysis, the solubility, oil binding capacity, zeta potential, emulsification properties, and in vitro antioxidant capacity, anti-carcinogenic and antidiabetic properties of hydrolysates were determined. FTIR combined with hierarchical cluster analysis (HCA) made in Amide I region enable to classification of the samples based on the changes of the secondary structure depending on the enzyme type and degree of fragmentation. Technological properties of NaCas were enhanced through the enzymatic hydrolysis, and those were more prominent in serine-type enzymes, regardless of the enzyme type, all hydrolysates showed high antioxidant capacities. All hydrolysates, specifically those produced by savinase and alcalase, reduced the viability of the carcinogenic Caco-2 cells in a dose-dependent manner and showed a very low level of cytotoxicity against healthy HEK-293 cells. The hydrolysis treatment made a significant contribution to the antidiabetic activity of NaCas. Particularly alcalase and savinase hydrolysates suppressed the activity of α- amylase and α- glucosidase. Therefore, the generated milk protein hydrolysates could be used in functional food developments for specific dietary purposes.

Vibrational and fluorescence spectroscopy to study gluten and zein interactions in complex dough systems

The volume-spanning network formed by gluten during breadmaking is crucial in the production of high-quality bakery products. Zein proteins are also capable of forming a protein network under specific conditions. Vibrational (Fourier transform infrared spectroscopy (FTIR) and Raman scattering) and fluorescence spectroscopy are powerful, non-invasive techniques capable of assessing protein structures and interactions. The main objective of this project was to explore the suitability of these techniques to study zein and gluten structures and interactions in complex dough systems. The dough samples were prepared by mixing 20 w/w% of protein (with different proportions of zein and gluten) and 80 w/w% of corn starch. The tyrosine (Tyr) fluorescence emission peak (λ_exc = 280 nm) was still present even in those zein-gluten samples containing the highest gluten concentration and lowest zein concentration. This suggests that the Tyr moieties (stemming from zein) are not in close proximity to tryptophan (Trp) of gluten and their fluorescence is not quenched efficiently. Raman scattering results also showed the presence of different Tyr residues, exposed and buried, as well as different conformations of disulfide bridges, in zein and gluten samples. Based on the results from spectroscopic measurements and scanning electron microscopy (SEM), two distinct network structures composed of gluten and zein were identified in the mixed dough systems. The present work illustrates how complementary vibrational (Raman scattering and FTIR) and fluorescence spectroscopy methods can be combined to non-invasively assess protein structure and interactions in a complex food matrix.

•

Exploration of non-invasive techniques to study proteins in complex food systems.

•

Complementary information obtained on protein structure at several length scales.

•

Zein dough viscoelasticity relates to the formation of beta-sheet rich fibrils.

•

Gluten and zein form two distinct network structures in dough making.

•

Zein inclusion increases water availability for gluten in gluten-zein dough.

A novel l-cysteine sensor using in-situ electropolymerization of l-cysteine: Potential to simple and selective detection

This work presents an all-in-one origami paper-based electrochemical platform for simple and inexpensive l-cysteine (Cys) detection using Cys as a monomer for modifying electrode surfaces. The proposed method combines the steps of electropolymerization and detection into a single device to offer a highly convenient method for the end-user. In comparison, the sensitivity toward Cys detection is a significantly increased using this modified electrode. The developed device provided a linear concentration range of 10-800 μM with a limit of detection of 5.5 μM. For application, the device was successfully applied to detect Cys in different food products such as wheat flour, bread, and cake with satisfactory results, yielding excellent intra-day and inter-day relative standard deviations (1.5-4.9%) and recoveries (84.2-110.8%). This discovery is important from the viewpoint of the development of Cys detection in other applications in the future.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

Toward Food Freshness Monitoring: Coordination Binding-Based Colorimetric Sensor Array for Sulfur-Containing Amino Acids

Herein, a self-assembled colorimetric chemosensor array composed of off-the-shelf catechol dyes and a metal ion (i.e., Zn2+) has been used for the sulfur-containing amino acids (SCAAs; i.e., glutathione, glutathione disulfide, L-cysteine, DL-homocysteine, and L-cystine). The coordination binding-based chemosensor array (CBSA) fabricated by a competitive assay among SCAAs, Zn2+ ions, and catechol dyes [i.e., pyrocatechol violet (PV), bromopyrogallol red (BPR), pyrogallol red (PR), and alizarin red S (ARS)] yielded fingerprint-like colorimetric changes. We succeeded in the qualification of SCAAs based on pattern recognition [i.e., a linear discrimination analysis (LDA)] with 100% correct classification accuracy. The semiquantification of reduced/oxidized forms of SCAAs was also performed based on LDA. Furthermore, we carried out a spike test of glutathione in food samples using the proposed chemosensor array with regression analysis. It is worth mentioning that we achieved a 91-110% recovery rate in real sample tests, which confirmed the accuracy of the constructed model. Thus, this study represents a step forward in assessing food freshness based on supramolecular analytical methods.

Synthesis and Characterization of a Bioconjugate Based on Oleic Acid and L-Cysteine

One of the main challenges facing materials science today is the synthesis of new biodegradable and biocompatible materials capable of improving existing ones. This work focused on the synthesis of new biomaterials from the bioconjugation of oleic acid with L-cysteine using carbodiimide. The resulting reaction leads to amide bonds between the carboxylic acid of oleic acid and the primary amine of L-cysteine. The formation of the bioconjugate was corroborated by Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and nuclear magnetic resonance (NMR). In these techniques, the development of new materials with marked differences with the precursors was confirmed. Furthermore, NMR has elucidated a surfactant structure, with a hydrophilic part and a hydrophobic section. Ultraviolet-visible spectroscopy (UV-Vis) was used to determine the critical micellar concentration (CMC) of the bioconjugate. Subsequently, light diffraction (DLS) was used to analyze the size of the resulting self-assembled structures. Finally, transmission electron microscopy (TEM) was obtained, where the shape and size of the self-assembled structures were appreciated.

Au–Ag core–shell composite nanoparticles as a selective and sensitive plasmonic chemical probe for l-cysteine detection in Lens culinaris (lentils)

The present work reported is a simple and selective method for the colorimetrical detection of l-cysteine in Lens culinaris (or lentils) using Au–Ag core–shell (Au core Ag shell) composite nanoparticles as a chemical probe.

The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review

Halal is an Arabic term used to describe any components allowed to be used in any products by Muslim communities. Halal food and halal pharmaceuticals are any food and pharmaceuticals which are safe and allowed to be consumed according to Islamic law (Shariah). Currently, in line with halal awareness, some Muslim countries such as Indonesia, Malaysia, and Middle East regions have developed some standards and regulations on halal products and halal certification. Among non-halal components, the presence of pig derivatives (lard, pork, and porcine gelatin) along with other non-halal meats (rat meat, wild boar meat, and dog meat) is typically found in food and pharmaceutical products. This review updates the recent application of molecular spectroscopy, including ultraviolet-visible, infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies, in combination with chemometrics of multivariate analysis, for analysis of non-halal components in food and pharmaceutical products. The combination of molecular spectroscopic-based techniques and chemometrics offers fast and reliable methods for screening the presence of non-halal components of pig derivatives and non-halal meats in food and pharmaceutical products.

Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil

This study presents a method to determine adulteration of olive oil (obtained from Olea europea, i.e. olives) with rapeseed oil (obtained from Brassica napus) or with corn oil (also named maize oil, obtained from Zea mays, i.e. maize) using Raman spectroscopy and a mathematical method based on exponential equation fit. The samples were prepared by mixing olive oil with volume fractions (0-100%) of rapeseed or corn oil. The oils were differentiated spectroscopically using intensity ratio for specific Raman peaks; Raman spectroscopy is able to detect changes within a liquid molecular environment without the need for sample treatment. It was possible to determine rapeseed or corn oil volume fractions added into the olive oil using the method proposed. Thus, the potential of Raman spectroscopy as a technique for determining adulteration of olive oil was corroborated clearly, opening the potential to investigate adulteration of other liquid foods, without any need for sample preparation.

Detection of lard in butter using Raman spectroscopy combined with chemometrics

There is a contentious need for robust and rapid methodologies for maintaining the authenticity of foods and food additives. The current paper presented a new Raman spectroscopy-based methodology for detection and quantification of lard in butter. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully performed for the classification and discrimination of butter and lard-adulterated samples. Strong discrimination pattern was observed in the HCA analysis. Also, partial least squares regression and principal component regression (R² = 0.99) were applied for quantification of lard in butter samples. Quite favorable prediction capabilities were observed in the cross-validation of PLS and PCR analysis for the adulteration levels between 0% and 100% lard fat (w/w). Raman spectroscopy coupled chemometrics was employed effectively for quantification of lard fat in butter fat samples with easy, robust, effective, low-cost and reliable application in the quality control of butter.

Comprehensive analysis of volatile compounds in cold-pressed safflower seed oil from Xinjiang, China

Three varieties of safflower seed oil (SSO) from Xinjiang Autonomous Region, China, were analyzed by headspace solid-phase micro-extraction gas chromatography coupled with mass spectrometry (HS-SPME-GC-MS) to reveal volatile components. Overall, 67 volatile components were determined and four compounds including isoamyl alcohol, caproic acid, n-pentanal, and heptanal were newly identified in SSO as aroma-active components. Meanwhile, 16 compounds were selected by relative odor activity value (ROAV) to evaluate contributions of single compounds to the overall odor (ROAV > 1), in which nonanal, (Z)-6-nonenal, and (E)-2,4-decadienal were the top three contributed substances (ROAV > 70). The sensory panel was described as eight definition terms (grassy, fruity, almond, mushroom, fatty, sweet, paddy, and overall fragrance). Principal component analysis (PCA) revealed a significant separation of three cultivars with the first principal component (PC-1) and the second principal component (PC-2) expressing 73.9% and 23.1%, respectively. Both PCA and ROAV allowed identifying the compounds positively correlated to sensory evaluation.

The effect of temperature on sulfate release from Pearl River sediments in South China

Sulfate (SO₄^2-) has received attention as means of monitoring water quality and pollution. However, the SO₄^2- content of rivers, lakes, and reservoirs varies significantly by season, so environmental factors such as temperature can affect it. An experiment was conducted with a series of aerobic and anaerobic tanks containing Pearl River sediments and distilled water to assess the release of SO₄^2- from sediments under controlled conditions. "Black-odor river" refers to near anoxic conditions in the water column and foul odors emanating from affected rivers in southeastern China. These river systems typical have sediments containing ammonia (NH₃), hydrogen sulfide (H₂S), and organic sulfide compounds in excess, and precipitates of sulfide (S^2-), with ferrous (Fe²⁺) or manganese (Mn²⁺). SO₄^2- concentration was measured at various depths in pore water and in the water column while controlling temperature and dissolved oxygen (DO) concentrations. Interpolation of study results revealed that SO₄^2- content was highest between temperatures of 20 °C and 25 °C. The relationship between SO₄^2- concentration, which varied with temperature and time, was fit using a linearized Michaelis-Menten function (R² = 0.69). The release of SO₄^2- to the water column was accelerated during the experiment (for temperatures higher than 20 °C), and led to higher SO₄^2- content in the water column than in pore water. The maximum concentration of SO₄^2- within the sediment occurred at a temperature of 20 °C. Comparing aerated and non-aerated tanks at 20 °C, we found that O₂ restricted SO₄^2- content in the water column; DO could, in turn, also be controlled by temperature. Fe²⁺ and Mn²⁺ had a negative correlation with SO₄^2-.

Raman Spectroscopy Study of Phosphorites Combined with PCA-HCA and OPLS-DA Models

Phosphorite is a nonrenewable strategic resource, a convenient and rapid method of phosphorite grade identification and classification is important to improve phosphate utilization. In this study, Raman spectroscopy has been combined with principal component analysis and hierarchical clustering analysis (PCA-HCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models for an investigation of different grade phosphorite samples. Both the PCA-HCA and OPLS-DA models showed that different grade phosphorite samples can be clearly distinguished by statistically analyzing the Raman spectra after smoothing, baseline correction, and first derivation. In addition, the S-line study on the OPLS-DA model clearly demonstrated that the symmetrical stretching vibrational mode of phosphate near 960 cm−1 had a much more significant contribution than other vibrational modes for the differentiation of different grade phosphorite samples.

Raman chemical feature extraction for quality control of dairy products

As a fast information acquisition technique, Raman spectroscopy can be used to control the quality of dairy products. Feature extraction is a necessary processing step to improve the efficiency of Raman spectral data. Principal component analysis is a traditional method that can effectively extract the features and reduce the dimension of spectral data. However, it is difficult to analyze the chemical information of the extracted feature, thus limiting its practical application. In this work, Raman spectral chemical feature extraction was carried out. The quality control of Dingxin dairy products (a famous dairy brand in China; purchased from Heilongjiang Zhaodong Tianlong Dairy Co. Ltd., Heilongjiang, China) was used as an example. Raman peak intensity, peak area, and peak ratio were extracted as chemical features and further investigated using Euclidean distance and the quality fluctuation control chart. The potential quality discrimination ability of the Raman feature extraction methods was demonstrated. The results showed that the Puzhen dairy products (purchased from Inner Mongolia Yinuo Halal Food Co. Ltd., Inner Mongolia, China) and Xueyuan dairy products (used as a control; purchased from Inner Mongolia Wulanchabu City Jining Xueyuan Dairy Co. Ltd., Inner Mongolia, China) could be distinguished from Dingxin dairy products when the Raman chemical features special peak intensity, peak area, and peak ratio were used, and their discriminatory ability increased in sequence. Hence, it was shown that Raman chemical feature extraction can achieve quality control and discriminant analysis of dairy products and that the spectral information is clear.

FeCo nanoparticles-embedded carbon nanofibers as robust peroxidase mimics for sensitive colorimetric detection of l-cysteine

A simple and low cost detection of l-cysteine is essential in the fields of biosensors and medical diagnosis. In this study, we have developed a simple electrospinning, followed by calcination process to prepare FeCo nanoparticles embedded in carbon nanofibers (FeCo-CNFs) as an efficient peroxidase-like mimic for the detection of l-cysteine. FeCo nanoparticles are uniformly dispersed within CNFs, and their diameters are highly influenced by the calcination temperature. The calcination temperature also influences the peroxidase-like catalytic activity, and the maximum activity is achieved at a calcination temperature of 550 °C. Owing to the high catalytic activity of the as-prepared FeCo-CNFs, a colorimetric technique for the rapid and accurate determination of l-cysteine has been developed. The detection limit is about 0.15 μM with a wide linear range from 1 to 20 μM. In addition, a high selectivity for the detection of l-cysteine over other amino acids, glucose and common ions is achieved. This study provides a simple, rapid and sensitive sensing platform for the detection of l-cysteine, which is a promising candidate for potential applications in biosensing, medicine, environmental monitoring.