Research article

Bioactive and nutritional compounds in fruits of pepper (Capsicum annuum L.) landraces conserved among indigenous communities from Mexico

  • Farmers' varieties or landraces of chili are regularly heterogeneous, selected and preserved by small traditional farmers and highly demanded by regional consumers. The objective of this study was to evaluate the variation in the content of phenolic compounds, vitamin C, carotenoids, capsaicinoids and antioxidant activity in fruits of a population collection of the landraces Huacle and De Agua, which originated in Oaxaca, Mexico, and a commercial variety of Jalapeño (control). The collection was grown in greenhouse conditions under a random block design. At harvest, a sample of ripe fruits was obtained to evaluate the content of phenolic compounds, vitamin C and antioxidant activity by UV–visible spectrophotometry and the concentration of capsaicin and dihydrocapsaicin was measured by high-resolution liquid chromatography. Significant differences were observed between the Huacle and De Agua landraces and between these and Jalapeño. The studied fruits exhibit the following pattern for flavonoid and carotenoid contents: Huacle > De Agua > Jalapeño. The opposite pattern was observed for total polyphenol and vitamin C contents: Jalapeño > De Agua > Huacle. The general pattern for capsaicinoids in fruits was Jalapeño > De Agua > Huacle. Huacle and De Agua populations showed high variability in all compounds evaluated, with positive correlations with antioxidant activity. The capsaicin content in Huacle populations varied ranging from 7.4 to 26.2 mg 100 g-1 and De Agua ranged from 12.4 to 46.8 mg 100 g-1.

    Citation: Rosalía García-Vásquez, Araceli Minerva Vera-Guzmán, José Cruz Carrillo-Rodríguez, Mónica Lilian Pérez-Ochoa, Elia Nora Aquino-Bolaños, Jimena Esther Alba-Jiménez, José Luis Chávez-Servia. Bioactive and nutritional compounds in fruits of pepper (Capsicum annuum L.) landraces conserved among indigenous communities from Mexico[J]. AIMS Agriculture and Food, 2023, 8(3): 832-850. doi: 10.3934/agrfood.2023044

    Related Papers:

    [1] Alma Mašić, Hermann J. Eberl . On optimization of substrate removal in a bioreactor with wall attached and suspended bacteria. Mathematical Biosciences and Engineering, 2014, 11(5): 1139-1166. doi: 10.3934/mbe.2014.11.1139
    [2] Maryam Basiri, Frithjof Lutscher, Abbas Moameni . Traveling waves in a free boundary problem for the spread of ecosystem engineers. Mathematical Biosciences and Engineering, 2025, 22(1): 152-184. doi: 10.3934/mbe.2025008
    [3] Fadoua El Moustaid, Amina Eladdadi, Lafras Uys . Modeling bacterial attachment to surfaces as an early stage of biofilm development. Mathematical Biosciences and Engineering, 2013, 10(3): 821-842. doi: 10.3934/mbe.2013.10.821
    [4] Ana I. Muñoz, José Ignacio Tello . Mathematical analysis and numerical simulation of a model of morphogenesis. Mathematical Biosciences and Engineering, 2011, 8(4): 1035-1059. doi: 10.3934/mbe.2011.8.1035
    [5] Fabiana Russo, Alberto Tenore, Maria Rosaria Mattei, Luigi Frunzo . Multiscale modelling of the start-up process of anammox-based granular reactors. Mathematical Biosciences and Engineering, 2022, 19(10): 10374-10406. doi: 10.3934/mbe.2022486
    [6] Peter W. Bates, Jianing Chen, Mingji Zhang . Dynamics of ionic flows via Poisson-Nernst-Planck systems with local hard-sphere potentials: Competition between cations. Mathematical Biosciences and Engineering, 2020, 17(4): 3736-3766. doi: 10.3934/mbe.2020210
    [7] Chiu-Yen Kao, Yuan Lou, Eiji Yanagida . Principal eigenvalue for an elliptic problem with indefinite weight on cylindrical domains. Mathematical Biosciences and Engineering, 2008, 5(2): 315-335. doi: 10.3934/mbe.2008.5.315
    [8] Nikodem J. Poplawski, Abbas Shirinifard, Maciej Swat, James A. Glazier . Simulation of single-species bacterial-biofilm growth using the Glazier-Graner-Hogeweg model and the CompuCell3D modeling environment. Mathematical Biosciences and Engineering, 2008, 5(2): 355-388. doi: 10.3934/mbe.2008.5.355
    [9] Min Zhu, Xiaofei Guo, Zhigui Lin . The risk index for an SIR epidemic model and spatial spreading of the infectious disease. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1565-1583. doi: 10.3934/mbe.2017081
    [10] Blaise Faugeras, Olivier Maury . An advection-diffusion-reaction size-structured fish population dynamics model combined with a statistical parameter estimation procedure: Application to the Indian Ocean skipjack tuna fishery. Mathematical Biosciences and Engineering, 2005, 2(4): 719-741. doi: 10.3934/mbe.2005.2.719
  • Farmers' varieties or landraces of chili are regularly heterogeneous, selected and preserved by small traditional farmers and highly demanded by regional consumers. The objective of this study was to evaluate the variation in the content of phenolic compounds, vitamin C, carotenoids, capsaicinoids and antioxidant activity in fruits of a population collection of the landraces Huacle and De Agua, which originated in Oaxaca, Mexico, and a commercial variety of Jalapeño (control). The collection was grown in greenhouse conditions under a random block design. At harvest, a sample of ripe fruits was obtained to evaluate the content of phenolic compounds, vitamin C and antioxidant activity by UV–visible spectrophotometry and the concentration of capsaicin and dihydrocapsaicin was measured by high-resolution liquid chromatography. Significant differences were observed between the Huacle and De Agua landraces and between these and Jalapeño. The studied fruits exhibit the following pattern for flavonoid and carotenoid contents: Huacle > De Agua > Jalapeño. The opposite pattern was observed for total polyphenol and vitamin C contents: Jalapeño > De Agua > Huacle. The general pattern for capsaicinoids in fruits was Jalapeño > De Agua > Huacle. Huacle and De Agua populations showed high variability in all compounds evaluated, with positive correlations with antioxidant activity. The capsaicin content in Huacle populations varied ranging from 7.4 to 26.2 mg 100 g-1 and De Agua ranged from 12.4 to 46.8 mg 100 g-1.



    The electronic medical records (EMRs), sometimes called electronic health records (EHRs) or electronic patient records (EPRs), is one of the most important types of clinical data and often contains valuable and detailed patient information for many clinical applications. This paper studies the technology of structuring EMRs and medical information extraction, which are key foundations for health-related various applications.

    As a kind of medical information extraction technology, the medical assertion classification (MAC) in EMRs, which is formally defined for the 2010 i2b2/VA Challenge, aims to recognize the relationship between medical entities (Disease and Symptom) and patients. Given a medical problem or entity mentioned in a clinical text, an assertion classifier must look at the context and choose the status of how the medical problem pertains to the patient by assigning one of seven labels: present, absent, conditional, possible, family, occasional, or history. The assertion is reflected in two aspects: whether the entity occurs to the patient, and how the entity occurs to the patient. As the basis of medical information processing, assertion classification in EMRs is of great importance to many EMRs mining tasks. When there are many researches about MAC for English EMRs, few studies have been done on Chinese texts.

    Based on the above, we study the MAC methods for Chinese EMRs in this paper. According to the corresponding task of the 2010 i2b2/VA Challenge, we divide the assertion categories of medical entity into seven categories: present (当前的), possible (可能的), conditional (有条件的), family (非患者的), occasional (偶有的), absent (否认的) and history (既往的). The definitions and Chinese sentence examples of different kind of assertion category are shown in the Table 1.

    Table 1.  Definition of assertion categories.
    Category Definition Example sentence
    Present Symptoms or illness that must be present in the patient 患者意识昏迷8小时(The patient was unconscious for 8 hours.)
    Absent A denial of illness or symptoms 患者无眩晕头痛(No vertigo, no headache)
    Conditional A condition or illness that occurs only under certain conditions 饮酒后易休克(Shock after drinking)
    Possible Possible disease or symptom 术后可能有红肿现象(Postoperative redness may occur)
    肿瘤待查(Tumor waiting for investigation)
    Family A condition or condition that is not the patient's own 直系亲属患有癫痫病史(History of epilepsy in immediate relatives)
    Occasional An illness or symptom that does not currently occur frequently 偶有头晕症状(Occasionally dizziness)
    History Past illnesses or symptoms 2年前因痛风入我院治疗(Gout was admitted to our hospital two years ago for treatment)

     | Show Table
    DownLoad: CSV

    Although there have been lot of methods proposed for recognizing entity assertion category from EMRs, most of them used traditional machine methods such as Support Vector Machine (SVM) and Conditional Random Field (CRF), which are mainly based on feature engineering. However, feature engineering is relatively time-consuming and costly, and resulting feature sets are both domain and model-specific. While deep neural network approaches on various medical information extraction tasks have achieved better performance compared to traditional machine learning models, research on entity assertion classification of EMRs using deep neural network model is still few.

    Therefore, this paper proposes a novel model for MAC of Chinese EMRs. We build a deep network (called GRU-Softmax) as baseline, which combines Gated Recurrent Unit (GRU) neural network (a type of Recurrent Neural Networks, RNNs) and softmax, to classify named entity assertion from Chinese EMR. Compared with RNN, the advantage of GRU-Softmax lie in that GRU neural networks have strong expressive ability to capture long context without time-intensive feature engineering.

    Furthermore, in order to obtain character level characteristics in EMRs text, we train Chinese character-level embedding representation using Convolutional Neural Network (CNN), and combine them with word-level embedding vector acquired from large-scale background training corpus. Then the combined vectors are sent to GRU architecture to train entity assertion classification model. In addition, to enhance the representation and distinguish ability of characters and their contexts, we integrate the medical knowledge attention (MKA) learned from entity names and their definition or descriptions in medical dictionary bases (MDBs) in the model.

    On the whole, the contributions of this work can be summarized as follow: (1) we introduce a deep neural network that combines GRU neural networks and Sfotmax to classify medical assertion at first time; (2) We compared the influence of character-level representation extracted by CNN on the model; (3) we use medical knowledge attention (MKA) to integrate entity representation from external knowledge (medical dictionary bases, MDBs).

    The remainder of this paper is composed as follows. In section 2 we summarize the related work about MAC. In section 3 we present our attention-based CNN-GRU-Softmax network model for MAC in Chinese EMRs. In section 4 we show the experimental results and give some analysis. Finally, we summarize our work and outline some ideas for future research.

    Research of entity assertion classification is to study the relation classification between entity and patient on the basis of entity known. Chapman et al. [1] proposed a classification model named as NegEx based on regular expression rules, which classifies disease entity as "existing" or "nonexistent", and can obtain 85.3% of F value on more than 1000 disease entities. Based on NegEx method and combining regular expression rules and trigger words, Harkema et al. [2] proposed the ConText method to classify the disease entities into one of six categories. On six different types of medical records, 76% to 93% of F values could be obtained, indicating that the distribution of modified disease entities varied greatly in different styles of texts.

    Based on the evaluation data of the 2010 i2b2 Challenge, researchers proposed many classification methods based on rules, SVM, CRF, etc. The most effective concept extraction systems used support vector machines (SVMs) [3,4,5,6,7,8,9,10,11], either with contextual information and dictionaries that indicate negation, uncertainty, and family history [6,10], or with the output of rule-based systems [3,6,8]. Roberts et al. [4] and Chang et al. [11] utilized both medical dictionary and rules. Chang et al. complemented SVM with logistic regression, multi-logistic regression, and boosting, which they combined using voting mechanism. The highest classification effect in the evaluation was obtained by the Bi-level classifier proposed by de Bruijn et al. [5], who used the cTAKES knowledge base created an ensemble whose final output was determined by a multi-class SVM, and the evaluation result F could reach 93.6%. Clark et al. [12] used a CRF model to determine negation and uncertainty with their scope, and added sets of rules to separate documents into different zones, to identify and scope cue phrases, and determine phrase status. They combined the results from the found cues and the phrase status module with a maximum entropy classifier that also used concept and contextual features.

    In this paper, we propose a neural network architecture combining GRU-CNN-Softmax network with Medical Knowledge Attention that will learn the shared semantics between medical record texts and the mentioned entities in the medical dictionary bases (MDBs). The architecture of our proposed model is shown in Figure 1. After querying pretrained character embedding tables, the input sentence will be transformed respectively to the corresponding sequences of pretrained character embeddings and random generated character embedding matrixes for every character. Then a CNN is used to form the character level representation and a GRU is used to encode the sentence representation after concatenating the pretrained character embeddings and character-level representation of the sentence. Afterwards, we treat the entity information from MKBs as a query guidance and integrate them with the original sentence representation using a multi-modal fusion gate and a filtering gate. At last, a Softmax layer is used to classify.

    Figure 1.  The framework of our model. The right part is the GMF and Filtering Gate.

    As described in Figure 2, we firstly train Chinese character embeddings from a large unlabeled Chinese EMR corpus, then CNN is used to generate sentence character-level representation from the character embedding matrix sequence to alleviate rare character problems and capture helpful morphological information like special characters in EMRs. Since the length of sentences is not consistent, a placeholder (padding) is added to the left and right side of character embeddings matrix to make the length of every sentence character-level representation vector matrix sequence equal.

    Figure 2.  Character-level representation of a sentence by CNN.

    The Gate Recurrent Unit (GRU) is a branch of the Recurrent Neural Network (RNN). Like LSTM, it is proposed to solve such problems as the gradient in long-term memory and reverse propagation. We choose to use GRU [13] in our model since it performs similarly to LSTM [14] but is computationally cheaper.

    The GRU model is defined by the following equations:

    zt=σ(Wzxt+Uzht1+bz) (1)
    rt=σ(Wrxt+Urht1+br) (2)
    ˜ht%=tanh(Whxt+Uh(ht1rt)+bh) (3)
    ˜ht%=tanh(Whxt+Uh(ht1rt)+bh) (4)

    In particular, zt and rt are vectors corresponding to the update and reset gates respectively, where * denotes elementwise multiplication. The activations of both gates are elementwise logistic sigmoid functions σ(), constraining the values of zt and rt ranging from 0 to 1. ht represents the output state vector for the current time framet, while ˜ht% is the candidate state obtained with a hyperbolic tangent. The network is fed by the current input vectorxt(sentence representation of previous layer), and the parameters of the model are Wz, Wr, Wh (the feed-forward connections), Uz, Ur, Uh(the recurrent weights), and the bias vectors bz, br, bh. The Gate Recurrent Unit (GRU) is shown in Figure 3.

    Figure 3.  The architecture of gate recurrent unit (GRU).

    Concerning rich entity mention and definition information containing in MDBs, the medical knowledge attention is applied to integrate entity representations learned from external knowledge bases as query vector for encoding. We use a medical dictionary to encode entity information (entity mention and definition) into attention scores as entity embeddings.

    at=f(eWAht) (5)

    Where e is the embedding for entity, and WA is a bi-linear parameter matrix. We simply choose the quadratic function f(x) = x2, which is positive definite and easily differentiate.

    Based on the output of GRU and attention scoring, we design a gated multimodal fusion (GMF) method to fuse the features from output of hidden layerhtand attention scoringat. When predicting the entity tag of a character, the GMF trades off how much new information of the network is considering from the query vector with the EMR text containing the character. The GMF is defined as:

    hat=tanh(Watat+bat) (6)
    hht=tanh(Whtht+bht) (7)
    gt=σ(Wgt(hathht)) (8)
    mt=gthat+(1gt)hht (9)

    where Wat, Wht, Wgt are parameters, hht and hat are the new sentence vector and new query vector respectively, after transformation by single layer perceptron. is the concatenating operation, σ is the logistic sigmoid activation, gt is the gate applied to the new query vector hht, and mt is the multi-modal fused feature from the new medical knowledge feature and the new textual feature.

    When decoding the combination of the multimodal fusion feature mt at position t, the impact and necessity of the external medical knowledge feature for different assertion is different. Because the multimodal fusion feature contains external knowledge feature more or less and it may introduce some noise. We therefore use a filtering gate to combine different features from different signal that better represent the useful information. The filtering gate is a scalar in the range of [0, 1] and its value depends on how much the multimodal fusion feature is helpful to label the tag of the assertion. stand the input feature to the decoder ˆmt are defined as follows:

    st=σ(Wst,htht(Wmt,stmt+bmt,st)) (10)
    ut=st(tanh(Wmtmt+bmt)) (11)
    ˆmt=Wˆmt(htut) (12)

    where Wmt,st, Wst,ht, Wmt, Wˆmt are parameters, ht is the hidden state of bidirectional LSTM at time t, ut is the reserved multimodal features after the filtering gate filter out noise, andis the concatenating operation. The architecture of gated multimodal fusion and filtering gate are shown in Figure 1.

    After we get the representation ˆmt of sentence, we use softmax function to normalize and output entity assertion probability.

    In this section, we evaluate our method on a manually annotated dataset. Following Nadeau et al., we use Precision, Recall, and F1 to evaluate the performance of the models [18].

    We use our own manually annotated corpus as evaluation dataset, which consists of 800 de-identified EMR texts from different clinical departments of a grade-A hospital of second class in Gansu Province. The annotated entity number of every entity assertion category in the dataset is shown in the Table 2.

    Table 2.  Number statistics of different entity assertion categories in the evaluation dataset.
    Category Training Test Total
    Present (当前的) 2025 1013 3038
    Absent (否认的) 1877 921 2799
    Conditional (有条件的) 204 102 306
    Possible (可能的) 844 420 1264
    Family (非患者本人的) 235 117 352
    Occasional (偶有的) 249 147 396
    History (既往的) 342 171 513
    Total 5778 2889 8667

     | Show Table
    DownLoad: CSV

    We use Google's Word2Vec to train Chinese character embeddings on our 30 thousand unlabeled Chinese EMR texts which is from a grade-A hospital of second class in Gansu Province. Random generated character embeddings are initialized with uniform samples from[3dim,3dim], where we set dim = 30.

    Table 3 gives the chosen hyper-parameters for all experiments. We tune the hyper-parameters on the development set by random search. We try to share as many hyper-parameters as possible in experiments.

    Table 3.  Parameter Setting.
    Parameter Value
    Character-level representation size 50
    Pretrained character Embedding Size 100
    Learning Size 0.014
    Decay Rate 0.05
    Dropout 0.5
    Batch Size 10
    CNN Window Size 3
    CNN Number of filters 50

     | Show Table
    DownLoad: CSV

    In this part, we describe all of models in the following experimental comparison.

    GRU+Softmax: We combine gated recurrent unit (GRU) neural network and Sfotmaxto classify assertion of clinical named entity. In this model, the GRU neural network is used to help encoding character embedding vector and then the Softmax layer is used to decode and classify. To compare the impact of different methods on experimental performance, we will use this model as the baseline.

    CNN+GRU+Softmax: This model is similar to the CNN-LSTM-CRF which was proposed by Ma and Hovy (2016)[15] and is a truly end-to-end system.

    CGAtS(CNN+GRU+Attention+Softmax): This model is the CNN-GRU-Softmax architecture enhanced by medical knowledge attention (MKA). In this model the output of hidden layer h and the attention score a are used to encode text representation as follows:

    c=Li=1aihi (13)

    where L is the window size of text characters.

    CGAtFuFiS(CNN+GRU+Softmax+All): This is our model. Unlike the previous one, we employed a gated multi-modal fusion (GMF) mechanism and a filtration gate.

    The performance on each of seven categories obtained by all models are shown in Figure 4, and their overall performance on the evaluation dataset is shown in Table 4.

    Figure 4.  Experimental results of different assertion categories.
    Table 4.  Performance of different models on the total evaluation dataset.
    Model Precision (%) Recall (%) F1 (%)
    Softmax 89.13 88.31 88.72
    GRU+Softmax(Baseline) 90.21 90.77 90.49
    CNN-GRU-Softmax 92.95 89.65 91.27
    CGAtS 90.76 93.34 92.03
    CGAtFuFiS 92.19 93.48 92.84

     | Show Table
    DownLoad: CSV

    We compare our model with the baseline. Table 4 shows the overall assertion classification performance obtained by our method and others, from which we can see that our model CGAtFuFiS obtains the best F1-score of 92.84%.

    The experimental results of different models on our manually annotated datasets are shown in Table 4 and 5. Compared with the baseline model, all other models have improved performance and the updated neural network model is better than the traditional machine learning methods on the MAC task.

    The convolution layer in convolution neural network can well describe the local features of characters, and the most representative part of the local features can be further extracted through the pooling layer. Therefore, our experimental results show that CNN-GRU-Softmax model is superior to GRU-CRF model.The performance of the CGAtS model is better than CNN-GRU-Softmax. This result shows that, the rich information of entities and their corresponding semantic definition from MDBs is surely useful for MAC. CGAtFuFiS model is slightly better than CGAtS model and indicates that for the clinical NER task in Chinese EMRs it is helpful to fuse the features from EMR text context with the external knowledge dictionary utilizing gated multimodal fusion (GMF). Since supplement of external information in MKBs sometimes causes noise to the model, we therefore use a filtering gate to combine and weight different features. As shown by the experimental results, the filtering gate is helpful to improve the overall performance of our model.

    Due to the sublanguage characteristic of Chinese EMRs, the expression of clinical named entity is very different from those in general text. Using the entity information contained in the MABs as the classification query vector can lead the decoder to focus on the entity itself. We combine text itself and MABs features together with a multi-modal fusion gate as the query vector, then set up a filtering gate to filter out useless feature information. The experimental results show that our model CGAtFuFiS, which integrates CNN, GRU, medical knowledge attention, gated multimodal fusion, filtering gate, and Softmax, achieves the best F1 score on the evaluation corpus.

    In this work, we proposed a medical knowledge-attention enhanced neural clinical entity assertion classification model, which makes use of the external MABs in the way of attention mechanism. A gated multi-modal fusion module is introduced to decide how much MABs information is fused into the query vector at each time step. We further introduced a filtering gate module to adaptively adjust how much multi-modal information can be considered at each time step. The experimental results on the manually annotated Chinese EMR evaluation dataset show that our proposed approach improved the performance of MAC task obviously compared to other baseline models.

    In the future, we will explore a fine-grained clinical entity classification model for Chinese EMRs and method to extract entity semantic relation in Chinese EMRs.

    We would like to thank the anonymous reviewers for their valuable comments. The research work is supported by the National Natural Science Foundation of China (NO. 61762081, No.61662067, No. 61662068) and the Key Research and Development Project of Gansu Province (No. 17YF1GA016).

    All authors declare no conflicts of interest in this paper.



    [1] Krug AS, Drummond EBM, Van Tassel DL, et al. (2023) The next era of crop domestication starts now. Proc Natl Acad Sci 120: e2205769120. https://doi.org/10.1073/pnas.2205769120 doi: 10.1073/pnas.2205769120
    [2] FAOSTAT (2022) Crop and livestock statistics 2020 and 2021. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy. Available from: https://www.fao.org/faostat/en/#data/QCL.
    [3] Karim KMR, Rafii MY, Misran AB, et al. (2021) Current and prospective strategies in the varietal improvement of chilli (Capsicum annuum L.) specially heterosis breeding. Agronomy 11: 2217. https://doi.org/10.3390/agronomy11112217 doi: 10.3390/agronomy11112217
    [4] Khoury CK, Achicanoy HA, Bjorkman AD, et al. (2016) Origins of food crops connect countries worldwide. Proc R Soc B 283: 20160792. https://dx.doi.org/10.1098/rspb.2016.0792 doi: 10.1098/rspb.2016.0792
    [5] Perry L, Flannery KV (2007) Precolumbian use of chili peppers in the Valley of Oaxaca, Mexico. Proc Nat Acad Sci 104: 11905–11909. https://doi.org/10.1073/pnas.0704936104 doi: 10.1073/pnas.0704936104
    [6] Kraft KH, Brown CH, Nabhan GP, et al. (2014) Multiple lines of evidence for the origin of domesticated chili pepper, Capsicum annuum, in Mexico. Proc Natl Acad Sci 111: 6165–6170. https://doi.org/10.1073/pnas.1308933111 doi: 10.1073/pnas.1308933111
    [7] Taitano N, Bernau V, Jardón-Barbolla L, et al. (2019) Genome-wide genotyping of a novel Mexican chile pepper collection illuminates the history of landraces differentiation after Capsicum annuum L. domestication. Evol Appl 12: 78–92. https://doi.org/10.1111/eva.12651 doi: 10.1111/eva.12651
    [8] Votova EJ, Baral JB, Bosland PW (2005) Genetic diversity of chile (Capsicum annuum var. annuum L.) landraces from norther New Mexico, Colorado, and Mexico. Econ Bot 59: 8–17. https://doi.org/10.1663/0013-0001(2005)059[0008:GDOCCA]2.0.CO; 2 doi: 10.1663/0013-0001(2005)059[0008:GDOCCA]2.0.CO;2
    [9] González-Jara P, Moreno-Letelier A, Fraile A, et al. (2011) Impact of human management of the genetic variation of wild pepper, Capsicum annuum var. glabriusculum. PLoS One 6: e28715. https://doi.org/10.1371/journal.pone.0028715 doi: 10.1371/journal.pone.0028715
    [10] Vera-Guzmán AM, Chávez-Servia JL, Carrillo-Rodríguez JC, et al. (2011) Phytochemical evaluation of wild and cultivated pepper (Capsicum annuum L. and C. pubescens Ruiz & Pav.) from Oaxaca, Mexico. Chil J Agric Res 71: 578–585. http://dx.doi.org/10.4067/S0718-58392011000400013 doi: 10.4067/S0718-58392011000400013
    [11] Wahyuni Y, Ballester AR, Sudarmonowati E, et al. (2013) Secondary metabolites of Capsicum species and their importance in the human diet. J Nat Prod 76: 783–793. https://doi.org/10.1021/np300898z doi: 10.1021/np300898z
    [12] Cao S, Chen H, Xiang S, et al. (2015) Anti-cancer effects and mechanisms of capsaicin in chili peppers. Am J. Plant Sci 6: 3075–3081. http://doi.org/10.4236/ajps.2015.619300 doi: 10.4236/ajps.2015.619300
    [13] Chamikara MDM, Dissanayake DRRP, Ishan M, et al. (2016) Dietary, anticancer and medicinal properties of the phytochemicals in chili pepper (Capsicum spp.). Ceylon J Sci 45: 5–20. http://doi.org/10.4038/cjs.v45i3.7396 doi: 10.4038/cjs.v45i3.7396
    [14] Mazida MM, Salleh MM, Osman H (2005) Analysis of volatile aroma compounds of fresh chilli (Capsicum annuum) during stages of maturity using solid phase microextraction (SPME). J Food Comp Anal 18: 427–437. https://doi.org/10.1016/j.jfca.2004.02.001 doi: 10.1016/j.jfca.2004.02.001
    [15] Cázares-Sánchez E, Ramírez-Vallejo P, Castillo-González F, et al. (2005) Capsaicinoids and preference of use in different morphotypes of chili peppers (Capsicum annuum L.) of East-Central Yucatan. Agrociencia 39: 627–238.
    [16] Rodríguez-Burruezo A, Kollmannsberger H, González-Mas MC, et al. (2010) HS-SPME comparative analysis of genotypic diversity in the volatile fraction and aroma-contributing compounds of Capsicum fruits from the annuum-chinense-frutescens complex. J Agric Food Chem 58: 4388–4400. https://doi.org/10.1021/jf903931 doi: 10.1021/jf903931t
    [17] Ghasemnezhad M, Sherafati M, Payvast GA (2011) Variation in phenolic compounds, ascorbic acid and antioxidant activity of five coloured bell pepper (Capsicum annuum) fruits at two different harvest times. J Funct Foods 3: 44–49. https://doi.org/10.1016/j.jff.2011.02.002 doi: 10.1016/j.jff.2011.02.002
    [18] Arimboor R, Natarajan RB, Menon KR (2015) Red pepper (Capsicum annuum) carotenoids as a source of natural food colors: analysis and stability-a review. J Food Sci Technol 52: 1258–1271. https://doi.org/10.1007/s13197-014-1260-7 doi: 10.1007/s13197-014-1260-7
    [19] Eggink PM, Maliepaard C, Tikunov Y, et al. (2012) A taste of sweet pepper: Volatile and non-volatile chemical composition of fresh sweet pepper (Capsicum annuum) in relation to sensory evaluation of taste. Food Chem 132: 301–310. https://doi.org/10.1016/j.foodchem.2011.10.081 doi: 10.1016/j.foodchem.2011.10.081
    [20] Álvarez-Parrilla E, de la Rosa LA, Amarowicz R, et al. (2011) Antioxidant activity of fresh and processed Jalapeño and Serrano peppers. J Agric Food Chem 59: 163–173. https://doi.org/10.1021/jf103434u doi: 10.1021/jf103434u
    [21] Ornelas-Paz JJ, Martínez-Burrola JM, Ruiz-Cruz S, et al. (2010) Effect of cooking on the capsaicinoids and phenolics contents of Mexican peppers. Food Chem 119: 1619–1625. https://doi.org/10.1016/j.foodchem.2009.09.05 doi: 10.1016/j.foodchem.2009.09.054
    [22] Hwang IG, Shin YJ, Lee S, et al. (2012) Effects of different cooking methods on the antioxidant properties of red pepper (Capsicum annuum L.). Prev Nutr Food Sci 17: 286–92. https://doi.org/10.3746/pnf.2012.17.4.286 doi: 10.3746/pnf.2012.17.4.286
    [23] Hamed M, Kalita D, Bartolo ME, et al. (2019) Capsaicinoids, polyphenols, and antioxidant activities of Capsicum annuum: comparative study of the effect of ripening stage and cooking methods. Antioxidants 8: 364. https://doi.org/10.3390/antiox8090364 doi: 10.3390/antiox8090364
    [24] Thompson RQ, Phinney KW, Sander LC, et al. (2005) Reversed-phase liquid chromatography and argentation chromatography of the minor capsaicinoids. Anal Bioanal Chem 381: 1432–1440. https://doi.org/10.1007/s00216-005-3098-3 doi: 10.1007/s00216-005-3098-3
    [25] Cisneros-Pineda O, Torres-Tapia LW, Gutiérrez-Pacheco LC, et al. (2007) Capsaicinoids quantification in chili peppers cultivated in the state of Yucatan, Mexico. Food Chem 104: 1755–1760. https://doi.org/10.1016/j.foodchem.2006.10.076 doi: 10.1016/j.foodchem.2006.10.076
    [26] Wahyuni Y, Ballester AR, Sudarmonowati E, et al. (2011) Metabolite biodiversity in pepper (Capsicum) fruits of thirty-two diverse accessions: Variation in health-related compounds and implications for breeding. Phytochemistry 72: 1358–1370. https://doi.org/10.1016/j.phytochem.2011.03.016 doi: 10.1016/j.phytochem.2011.03.016
    [27] García-Jiménez FA, Romero-Castillo PA, Reyes-Dorantes A (2018) Presencia de carotenoides en chile ancho y pasilla (Capsicum annuum L.) en muestras de 10 años y recientes. Polibotánica 46: 259–272. https://doi.org/10.18387/polibotanica.46.17 doi: 10.18387/polibotanica.46.17
    [28] Sánchez-Toledano BI, Cuevas-Reyes V, Kallas Z, et al. (2021) Preferences in 'jalapeño' pepper attributes: A choice study in Mexico. Foods 10: 3111. https://doi.org/10.3390/foods10123111 doi: 10.3390/foods10123111
    [29] AOAC (2005) Association of Official Agricultural Chemists, Ash of flour. 17th, AOAC International Publisher, Gaithersburg, USA.
    [30] Dürüst N, Sümengen D, Dürüst Y (1997) Ascorbic acid and element contents of foods of Trabzon (Turkey). J Agric Food Chem 45: 2085–2087. https://doi.org/10.1021/jf9606159 doi: 10.1021/jf9606159
    [31] Singleton VL, Rossi JA (1965) Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. Am J Enol Vitic 16: 144–158. https://doi.org/10.5344/ajev.1965.16.3.144 doi: 10.5344/ajev.1965.16.3.144
    [32] Lin JY, Tang CY (2007) Determination of total phenolic and flavonoid contents in selected fruits and vegetables, as well as their stimulatory effects on mouse splenocyte proliferation. Food Chem 101: 140–147. https://doi.org/10.1016/j.foodchem.2006.01.014 doi: 10.1016/j.foodchem.2006.01.014
    [33] Brand-Williams W, Cuvelier ME, Berset CLWT (1995) Use of a free radical method to evaluate antioxidant activity. LWT-Food Sci Technol 28: 25–30. https://doi.org/10.1016/S0023-6438(95)80008-5 doi: 10.1016/S0023-6438(95)80008-5
    [34] Benzie IF, Strain JJ (1996) The ferric reducing ability of plasma (FRAP) as a measure of "antioxidant power": The FRAP assay. Anal Biochem 239: 70–76. https://doi.org/10.1006/abio.1996.0292 doi: 10.1006/abio.1996.0292
    [35] Othman ZAA, Ahmed YBH, Habila MA, et al. (2011) Determination of capsaicin and dihydrocapsaicin in Capsicum fruit samples using high performance liquid chromatography. Molecules 16: 8919–8929. https://doi.org/10.3390/molecules16108919 doi: 10.3390/molecules16108919
    [36] Juangsamoot J, Ruangviriyachai C, Techaeongstien S, et al. (2012) Determination of capsaicin and dihydrocapsaicin in some hot chilli varieties by RP-HPLC-PDA after magnetic stirring extraction and clean up with C18 cartridge. Int Food Res J 19: 1217–1226.
    [37] SAS Institute Inc. (SAS) (2006) Base SASⓇ 9.1.3 Procedures Guide (2nd edition, Volumes 1–4). SAS Institute Inc.: Cary, NC, USA.
    [38] Singh R (2016) Chemotaxonomy: A tool for plant classification. J Med Plants Stud 4: 90–93.
    [39] Hervert-Hernandez D, Sayago-Ayerdi SG, Goñi I (2010). Bioactive compounds of four hot pepper varieties (Capsicum annuum L.), antioxidant capacity, and intestinal bioaccessibility. J Agric Food Chem 58: 3399–3406. https://doig.org/10.1021/jf904220w doi: 10.1021/jf904220w
    [40] Martínez-Ispizua E, Martínez-Cuenca MR, Marsal JI, et al. (2021) Bioactive compounds and antioxidant capacity of Valencian pepper landraces. Molecules 26: 1031. https://doig.org/10.3390/molecules 26041031 doi: 10.3390/molecules26041031
    [41] Ionicǎ ME, Nour V, Trandafir I (2017) Bioactive compounds and antioxidant activity of hot pepper fruits at different stages of growth and ripening. J Appl Bot Food Qual 90: 232–237. https://doig.org/10.5073/JABFQ.2017.090.029 doi: 10.5073/JABFQ.2017.090.029
    [42] Lahbib K, Dabbou S, Bok SE, et al. (2017) Variation of biochemical and antioxidant activity with respect to the part of Capsicum annuum fruit Tunisian autochthonous cultivars. Ind Crop Prod 104: 164–170. https://doi.org/10.1016/j.indcrop.2017.04.037 doi: 10.1016/j.indcrop.2017.04.037
    [43] Vazquez-Flores AA, Góngora-Pérez O, Olivas-Orduña I, et al. (2020) Phytochemical profile and antioxidant activity of chiltepin chili (Capsicum annuum var. glabriusculum), Sonora, Mexico. J Food Bioact 11: 57–67. https://doi.org/10.31665/JFB.2020.11237 doi: 10.31665/JFB.2020.11237
    [44] Vera-Guzmán AM, Aquino-Bolaños EN, Heredia-García E, et al. (2017) Flavonoid and capsaicinoid contents and consumption of Mexican chili pepper (Capsicum annuum L.) landraces. In: Justino GC (Ed.), Flavonoids-from Biosynthesis to Human Health, London, UK, InTechOpen, 405–437. https://doi.org/10.5772/68076
    [45] Ribes-Moya AM, Adalid AM, Raigón MD, et al. (2020) Variation in flavonoids in a collection of peppers (Capsicum sp.) under organic and conventional cultivation: effect of the genotype, ripening stage, and growing system. J Sci Food Agric 100: 2208–2223. https://doi.org/10.1002/jsfa.10245 doi: 10.1002/jsfa.10245
    [46] Antonio AS, Wiedemann LSM, Junior VV (2018) The genus Capsicum: A phytochemical review of bioactive secondary metabolites. RSC Adv 8: 25767–25784. https://doi.org/10.1039/C8RA02067A doi: 10.1039/C8RA02067A
    [47] Espichán F, Rojas R, Quispe F, et al. (2022) Metabolomic characterization of 5 native Peruvian chili peppers (Capsicum spp.) as a tool for species discrimination. Food Chem 386: 132704. https://doi.org/10.1016/j.foodchem.2022.132704 doi: 10.1016/j.foodchem.2022.132704
    [48] Castillo-Velarde ER (2019) Vitamin C in health and disease. Rev Fac Med Hum 19: 95–100. https://doi.org/10.25176/RFMH.v19i4.2351 doi: 10.25176/RFMH.v19i4.2351
    [49] Rosa-Martínez E, García-Martínez MD, Adalid-Martínez AM, et al. (2021) Fruit composition profile of pepper, tomato and eggplant varieties grown under uniform conditions. Food Res Int 147: 110531. https://doi.org/10.1016/j.foores.2021.110531 doi: 10.1016/j.foodres.2021.110531
    [50] Agostini-Costa ST, da-Silva-Gomes I, de-Melo LAMP, et al. (2017) Carotenoid and total vitamin C content of peppers from selected Brazilian cultivars. J Food Comp Anal 57: 73–79. https://doi.org/10.1016/j.jfca.2016.12.020 doi: 10.1016/j.jfca.2016.12.020
    [51] Topuz A, Ozdemir F (2007) Assessment of carotenoids, capsaicinoids and ascorbic acid composition of some selected pepper cultivars (Capsicum annuum L.) grown in Turkey. J Food Comp Anal 20: 596–602. https://doi.org/10.1016/j.jfca.2007.03.007 doi: 10.1016/j.jfca.2007.03.007
    [52] Kim JS, Ahn J, Lee SJ, et al. (2011) Phytochemicals and antioxidant activity of fruits and leaves of paprika (Capsicum annuum L., var. Special) cultivated in Korea. J Food Sci 76: C193–C198. https://doi.org/10.1111/j.1750-3841.2010.01891.x doi: 10.1111/j.1750-3841.2010.01891.x
    [53] Deepa N, Kaur C, George B, et al. (2007) Antioxidant constituents in some sweet pepper (Capsicum annuum L.) genotypes during maturity. LWT-Food Sci Technol 40: 121–129. https://doi.org/10.1016/j.lwt.2005.09.016 doi: 10.1016/j.lwt.2005.09.016
    [54] Chávez-Mendoza C, Sanchez E, Muñoz-Marquez E, et al. (2015) Bioactive compounds and antioxidant activity in different grafted varieties of bell pepper. Antioxidants 4: 427–446. https://doi.org/10.3390/antiox4020427 doi: 10.3390/antiox4020427
    [55] Paredes-Andrade NJ, Monteros-Altamirano A, Tapia-Bastidas CG, et al. (2020) Morphological, sensorial and chemical characterization of chilli peppers (Capsicum spp.) from the CATIE genebank. Agronomy 10: 1732. https://doi.org/10.3390/agronomy10111732 doi: 10.3390/agronomy10111732
    [56] Medina-Juárez LÁ, Molina-Quijada DM, Sánchez CLDT, et al. (2012) Antioxidant activity of peppers (Capsicum annuum L.) extracts and characterization of their phenolic constituents. Interciencia 37: 588–593.
    [57] Rice-Evans CA, Miller NJ, Paganga G (1996) Structure-antioxidant activity relationships of flavonoids and phenolic acids. Free Radic Biol Med 20: 933–956. 10.1016/0891-5849(95)02227-9 doi: 10.1016/0891-5849(95)02227-9
    [58] Gardner PT, White TA, McPhail DB, et al. (2000) The relative contributions of vitamin C, carotenoids and phenolics to the antioxidant potential of fruit juices. Food Chem 68: 471–474.
    [59] Materska M, Perucka I (2005) Antioxidant activity of the main phenolic compounds isolated from hot pepper fruit (Capsicum annuum L.). J Agric Food Chem 53: 1750–1756. https://doi.org/10.1021/jf035331k doi: 10.1021/jf035331k
    [60] Kogure K, Goto S, Nishimura M, et al. (2002) Mechanism of potent antiperoxidative effect of capsaicin. Biochim Biophys Acta 1573: 84–92. https://doi.org/10.1016/s0304-4165(02)00335-5 doi: 10.1016/S0304-4165(02)00335-5
    [61] Sora GTS, Haminiuk CWI, da Silva MV, et al. (2015) A comparative study of the capsaicinoid and phenolic contents and in vitro antioxidant activities of the peppers of the genus Capsicum: an application of chemometrics. J Food Sci Technol 52: 8086–8094. https://doi.org/10.1007/s13197-015-1935-8 doi: 10.1007/s13197-015-1935-8
  • This article has been cited by:

    1. Babita Pandey, Devendra Kumar Pandey, Brijendra Pratap Mishra, Wasiur Rhmann, A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions, 2021, 13191578, 10.1016/j.jksuci.2021.01.007
    2. Lizong Deng, Luming Chen, Tao Yang, Mi Liu, Shicheng Li, Taijiao Jiang, Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study, 2021, 23, 1438-8871, e26892, 10.2196/26892
    3. Marta B. Fernandes, Navid Valizadeh, Haitham S. Alabsi, Syed A. Quadri, Ryan A. Tesh, Abigail A. Bucklin, Haoqi Sun, Aayushee Jain, Laura N. Brenner, Elissa Ye, Wendong Ge, Sarah I. Collens, Stacie Lin, Sudeshna Das, Gregory K. Robbins, Sahar F. Zafar, Shibani S. Mukerji, M. Brandon Westover, Classification of neurologic outcomes from medical notes using natural language processing, 2023, 214, 09574174, 119171, 10.1016/j.eswa.2022.119171
    4. Jin-ah Sim, Xiaolei Huang, Madeline R. Horan, Christopher M. Stewart, Leslie L. Robison, Melissa M. Hudson, Justin N. Baker, I-Chan Huang, Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review, 2023, 146, 09333657, 102701, 10.1016/j.artmed.2023.102701
    5. Yu Zhang, Rui Xie, Iman Beheshti, Xia Liu, Guowei Zheng, Yin Wang, Zhenwen Zhang, Weihao Zheng, Zhijun Yao, Bin Hu, Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN, 2024, 169, 00104825, 107873, 10.1016/j.compbiomed.2023.107873
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1600) PDF downloads(223) Cited by(0)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog