**1. Introduction**

Kudzu (*Pueraria lobata*) is a herbaceous, perennial plant native to East Asia and belongs to the Leguminosae family. It is a rich source of starch, dietary fiber, minerals, and isoflavonoids and possesses many nutritional and pharmacological activities [1–3], such as antioxidant, anticancer, anti-inflammation, and neuroprotective properties. Additionally, kudzu is used as a vegetable and edible ingredient in the preparation of various foods [4]. For example, kudzu starch is used in the production of noodles, beverages, oral liquids, and various dishes, and it serves as a sauce thickener [5]. Food products containing components of kudzu have begun to attract interest from both consumers and researchers due to their health benefits. However, enzymatic browning of kudzu during storage, handling, and processing negatively affects its sensory and nutritional properties. Polyphenol oxidase (PPO) is the primary enzyme involved in enzymatic browning [6,7]. Therefore, the inactivation of PPO is important in improving the quality attributes of kudzu products.

Thermal treatment has been widely used as a non-chemical means for enzyme inactivation in food processing. The thermal inactivation of PPO from fruits and vegetables has been well evaluated in mushrooms [8,9], pear [10,11], apple [12,13], chestnut kernel [14], and pineapple [15]. The thermostability of PPO highly depended on the environmental conditions, and an obvious difference was observed in food and model systems. Purified apple PPO was almost completely inactivated after incubation at 65 ◦C for 20 min [13], while PPO in apple slices still retained about 40% relative activity in the same condition [12].

**Citation:** Liu, J.; Zhang, J.; Liao, T.; Zhou, L.; Zou, L.; Liu, Y.; Zhang, L.; Liu, W. Thermal Inactivation Kinetics of Kudzu (*Pueraria lobata*) Polyphenol Oxidase and the Influence of Food Constituents. *Foods* **2021**, *10*, 1320. https://doi.org/10.3390/foods10061320

Academic Editor: Montserrat Dueñas Paton

Received: 14 May 2021 Accepted: 4 June 2021 Published: 8 June 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Compared to crude PPO extract from pear [11], PPO in pear puree [10] showed higher thermostability at a temperature range from 30 to 100 ◦C. The food system differs from the model system since the complex food constituents may affect the enzymatic properties during food processing [16]. The impact of pectin [17,18], protein [19,20], and sugar [21] on enzymatic properties have been investigated. However, there are few reports comparing the thermostability of PPO in food and model systems in the same study, and the effect of food constituents on the thermal inactivation kinetics remains unclear.

In this study, PPO from kudzu was purified and characterized. The thermal stability and inactivation kinetics of kudzu tissue PPO, crude PPO, and purified PPO were evaluated and compared with each other. To further illustrate the lower thermal inactivation effect in kudzu tissue, the content of food constituents, including pectin, protein, sucrose, and starch, in kudzu was analyzed, and the effect of these constituents on PPO activity, thermal inactivation kinetics, and conformation change was also evaluated.

#### **2. Materials and Methods**

#### *2.1. Materials*

Kudzu root (*Pueraria lobata*) was harvested from the plantation at Shangrao, Jiangxi province in eastern China, and stored at 4 ◦C until processing. Triton X-100, polyvinylpolypyrrolidone (PVPP), and *m*-hydroxydiphenyl were obtained from Aladdin Chemicals Co. (Shanghai, China). Catechol was obtained from Macklin Chemicals Co. (Shanghai, China). Pectin was purchased from Sigma-Aldrich Co. (Shanghai, China). Bovine serum albumin (BSA), starch, sucrose, D-Galacturonic acid, D-Glucose ammonium sulfate, tetramethylethylenediamine (TEMED), phenylmethylsulfonyl fluoride (PMSF), SDS-PAGE loading buffer, and standard protein mixture (Marker) were purchased from Solarbio Science and Technology Co. (Beijing, China). All the other chemicals were of analytical grade. Double-distilled water was used for preparing all the solutions.

#### *2.2. PPO Extraction*

The extraction and purification of PPO from kudzu were carried out according to the method of Liu et al. [22] with modifications. After washing and peeling, kudzu was sliced and mashed using a blender (HX-PB1053, AUX, Zhejiang, China) with 0.1 M phosphate buffer (pH 6.5) containing 2% (*w*/*v*) polyvinylpolypyrrolidone (PVPP), 0.5 M NaCl, and 1% (*v*/*v*) triton X-100 in a proportion of 1:1 (*v*/*v*) to yield puree. The mixture was stirred in an ice-water bath for 2 h and centrifuged (SORVALL Biofuge primo R, Thermo, Germany) for 20 min at 11,000× *g* at 4 ◦C. The supernatant was collected as the crude PPO (cPPO) and stored at 4 ◦C until use.


**3. Literature review**

#### **3.1 Typology of NBT activities and their interrelatedness**

Forms of tourism that fall under the ambit of NBT (as illustrated in **Figure 1**) are safari tourism, wildlife tourism, ecotourism, adventure tourism, agricultural tourism, protected area tourism, desert tourism, marine tourism, and arctic tourism [6, 35–38].

**Figure 1.** *The typology of NBT (source: Authors).*


**2. Methods**

#### *2.1. Nonlinear Band Dimensionality Expansion*

An early attempt to expand the original set of band images is to utilize nonlinear functions, for example, auto-correlation and cross-correlation, an idea derived from [16,20]. This type of NBE process is referred to as correlation band expansion process (CBEP). Combining these new CBEP-generated band images with the original set of band images produces a hyperspectral image with sufficient band images.

The CBEP presented in this section is an NBE process using correlation functions to generate new band images from the original set of multispectral images. Its original idea was developed in [16,20].

#### *Correlation Band Expansion Process* (CBEP)

Step 1. First-order band image: {**B***l*}*L l*=1 = set of original band images Step 2. Second-order correlated band images:

Step 3. Third order correlated band images

(i) . **B**3 *l*/*L l*=1= set of auto-correlated band images

Step 4. Other nonlinear correlated band images

It should be noted that, according to the nonlinear functions described in Steps (1)–(4), the band images generated by CBEP contain only nonlinear spectral information but not spatial information. In what follows, we develop an iterative CEM (ICEM) to address this issue where spatial information can be captured by using a Gaussian filter and feed it back to expand images currently being processed to create a new set of image data cubes.

## *2.2. Iterative CEM*

ICEM, presented in this section, is implemented in conjunction with CBEP in an iterative manner. More specifically, it utilizes CBEP to create new band images via an NBE process. Once CBEP process is completed, a new set of image data cubes is generated for CEM to perform subpixel target detection. To obtain class spatial information, a Gaussian filter is introduced in the CEM-detected maps so that spatial contextual information of data sample vectors can be captured by a Gaussian filter. The resulting Gaussian-filtered CEM-detection abundance fractional map is fed back to create a new band incorporated into NBE to form a new hyperspectral cube which will be further used for re-processing CEM again. The same process is repeated over and over again until a stopping rule is satisfied. This repeated implementation of CEM via feedback loops in an iterative fashion is ICEM.

Specifically, at each iteration, say *k*th iteration, a Gaussian filter is used to blur -- -**B**| (*k*) CEM which is the absolute value of CEM-detection abundance fractional map, **B**(*k*) CEM. This Gaussian-filtered band image, - - -**B**| (*k*) GF(CEM) provides spatial classification information as similar filters used in [23] and will be further fed back to **Ω**(*k*) NBE to create a new set of hyperspectral images, **Ω**(*k*+1) NBE = **Ω**(*k*) NBE ∪ , - - -**B**| (*k*) GF(CEM) - to be used by CEM again for next iteration. The same procedure is continued. To terminate the process, an automatic stopping rule is designed. It applies Otsu's method [21] to - - -**B**| (*k*) GF(CEM) to produce a binary classification map, **B**(*k*) binary that will be used to calculate DSI [22]. If two consecutive DSI values are within an error threshold, ICEM will be terminated and - - -**B**| (*k*) CEM and **B**(*k*) binary will be the desired final real-valued WMH lesion detection map for visual inspection and binary value detection maps of WMH lesions for quantitative analysis.

In the following, we describe detailed step-by-step implementation of ICEM in great detail.


**4. Ensemble strategies**

• Using ensemble methods as a preprocessing step before performing the essential environ-

• Clustering environmental documents according to their topics and main contents.

• Usage of process mining to improve work management in the environmental science.

Ensemble learning is a machine learning technique where multiple learners are trained to solve the same problem and their predictions are combined with a single output that probably has better performance on average than any individual ensemble member. The fundamental idea behind ensemble learning is to combine weak learners into one, a strong learner, who has a better generalization error and is less sensitive to overfitting in the presence of noise or small sample size. This is because different classifiers can sometimes misclassify different patterns and accuracy can be improved by combining the decisions of complementary classifiers.

A typical ensemble framework for classification tasks contains four fundamental components

• *Training set*: a training set is a special set of labeled examples providing known information

• *Base inducer*(s) or *base classifier*(s): an inducer is a learning algorithm that is used to learn from a training set. A base inducer obtains a training set and constructs a classifier that

• *Diversity generator*: it is clear that nothing is gained from an ensemble model if all ensemble members are identical. The diversity generator is responsible for generating the diverse classifiers and decides the type of every base classifier that differs from each other. Diversity can be realized in different ways depending on the accuracy of individual classifiers for the improved classification performance. Common diversity creation approaches are (i) using different training sets, (ii) combining different inducers, and (iii) using different

• *Combiner*: the task of the combiner is to produce the final decision by combining all classification results of the various base inducers. There are two main methods of combining: weighting methods and meta-learning methods. *Weighting methods* give each classifier a weight proportional to its strength and combine their votes based on these weights. The weights can be fixed or dynamically determined when classifying an instance. Common weighting methods are majority voting, performance weighting, Bayesian combination, and vogging. *Meta-learning methods* learn from new training data created from the predictions of a set of base classifiers. The most well-known meta-learning methods are stacking

generalizes relationship between the input features and the target outcome.

mental study.

6 Data Mining

**3. Background information**

**3.2. Elements of an ensemble classifier**

**3.1. Ensemble learning**

descripted as follows:

that are used for training.

parameters for a single inducer.

In order to construct an ensemble model, any of the following strategies can be performed:

### **4.1. Strategy 1: different training sets using random sampling with replacement**

One ensemble strategy is to train different base learners by different subsets of the training set. This can be done by random resampling of a dataset (i.e., *bagging*; **Figure 2a**). When we train multiple base learners with different training sets, it is possible to reduce variance and therefore error.

### **4.2. Strategy 2: different training sets obtained by random instance and feature subset selection**

The combination of bagged decision trees is constructed similar to Strategy 1 using one significant adjustment that random feature subsets are used (i.e., *random forest*; **Figure 2b**). When we have enough trees in the forest, random forest classifier is less likely overfit the model. It is also useful to reduce the variance of low-bias models, besides handling missing values easily.


**Conclusion**

IQoro neuromuscular training can relieve/improve esophageal dysphagia and reflux symptoms in adults, likely due to improved hiatal competence. The similarity of the results in the two groups suggests that many people suffer from Hiatus hernia despite this not having been confirmed by diagnosis.